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How Does Digital Trade Affect a Firm’s Green Total Factor Productivity? A Life Cycle Perspective

Sustainability 2025, 17(14), 6435; https://doi.org/10.3390/su17146435

by Jianbo Hu¹, Wenxin Cai¹, Yu Shen^2,* and Faustino Dinis¹

Reviewer 1:

Bashar Abu Khalaf

Reviewer 2: Anonymous

Reviewer 3:

Norbert Laurisz

Reviewer 4: Anonymous

Sustainability 2025, 17(14), 6435; https://doi.org/10.3390/su17146435

Submission received: 27 May 2025 / Revised: 6 July 2025 / Accepted: 11 July 2025 / Published: 14 July 2025

(This article belongs to the Special Issue Advanced, Digital, Sustainable Entrepreneurship and Innovation Management)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Kindly find attached report.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Reviewer #1：

Comments and Suggestions:

Comment 1: High Similarity Index: The manuscript shows a similarity rate of 32%, which raises serious concerns about originality and adherence to publication ethics. A thorough revision is required to ensure proper paraphrasing and citation.

Response 1: Thank you for raising this important concern. We acknowledge that in the original version, some parts of the manuscript, especially the literature review and methodology sections, may have included wording that was too similar to existing sources, even though proper citations were provided. In response, we have carefully revised the manuscript by paraphrasing relevant content, restructuring sentences, and ensuring that all citations fully comply with publication ethics. We also conducted an internal similarity check on the revised version using Turnitin, and we can confirm that the similarity index has now been substantially reduced to an acceptable level of 20%. We assure you that no intentional misconduct occurred, and we are grateful for your reminder to uphold the highest standards of academic integrity. We hope that the revised version fully addresses your concern.

Comment 2: Lack of Theoretical Framework: The manuscript fails to include a theoretical background section that justifies and explains the relationships between the core variables. This omission undermines the academic grounding of the study.

Response 2: Thank you very much for this valuable comment. In response, we have added a dedicated section on the theoretical foundation to enhance the conceptual grounding of the paper. Specifically, we now explain more clearly the mechanisms through which digital trade influences green total factor productivity (GTFP), drawing on sustainable development theory, innovation diffusion theory (Rogers, 2003), and the Technology–Organization–Environment (TOE) framework (Tornatzky & Fleischer, 1990). These theoretical perspectives provide a more rigorous explanation of how digital trade can promote green innovation, improve supply chain efficiency, and interact with institutional conditions to jointly advance productivity and environmental performance. We sincerely hope that these revisions adequately address your concern. For your convenience, we have included the revised sections below.

Theoretical foundation

2.1. Sustainable development theory

Sustainable development theory emphasizes the need to strike a balance between economic growth, environmental protection, and social welfare [18,19]. At the core of this paradigm is the concept of 'decoupling', which aims to maximize productivity while minimizing ecological degradation. Data Envelopment Analysis (DEA) is becoming increasingly popular for modeling sustainable development applications because it can quantify the overall efficiency of complex systems in a data-driven manner. DEA is particularly effective at addressing multi-objective conflicts, such as those between economic growth and environmental protection[20]. DEA is commonly used to measure energy efficiency, carbon total factor productivity, and other metrics [16,21]. The SBM-DEA model can address undesirable outputs by incorporating pollution into efficiency calculations, thereby accurately reflecting green productivity. GTFP is a key indicator of green productivity and is widely used in environmental research [22,23]. In this context, digital trade, an emerging, digitally driven business model, has the potential to transform production and consumption patterns to achieve more sustainable outcomes [8]. Digital trade can catalyze clean production processes, resource efficiency, and green innovation by reducing information asymmetry, facilitating knowledge exchange, and optimizing cross-border transactions. These factors are key drivers of GTFP improvement.

2.2. Innovation diffusion theory and technology-organization-environment theory

The theory of innovation diffusion supplements this viewpoint by detailing how new technologies and sustainable practices disseminate throughout businesses and industries [24]. Digital trade platforms accelerate the dissemination of green technologies and practices through greater trade liberalization, financial deepening, and knowledge spillovers due to their global reach and real-time data capabilities [25]. Additionally, businesses can use digital platforms to more effectively coordinate their supply chains, achieving the seamless integration of environmental standards and resource optimization across multiple stakeholders [26]. Conversely, digital service trade barriers hinder the development of production specialization[27], further highlighting digital trade's role in enhancing production efficiency. The Technology-Organization-Environment (TOE) framework further elucidates how these dynamics unfold at the firm level. It categorizes the factors influencing a firm's or organization's implementation of technological innovation into technological, organizational, and environmental dimensions. Using the TOE framework, one can study how technological, organizational, and urban environmental factors interact to influence the relationship between digital trade and GTFP.

This study examines three dimensions: technological, organizational, and environmental. The technological dimension represents an enterprise's green technological innovation capabilities. The organizational dimension represents its supply chain management capabilities. The environmental dimension represents the enterprise's life cycle stage (growth, maturity, or decline) and external environmental factors, such as government environmental regulations, intellectual property protection levels, and market integration levels. Technology acts as a driver, organizational capabilities determine adoption levels, and external environmental factors shape the broader institutional context. The study posits that DT primarily impacts GTFP through two channels: stimulating green technology innovation and improving supply chain management efficiency. These mechanisms are moderated by firm life cycle factors and environmental factors from government and market sources, ultimately driving the heterogeneity of the digital trade–GTFP relationship between firms and the environment. Figure 1 illustrates the theoretical framework based on sustainability, innovation diffusion, and TOE theories, showing how urban digital
trade influences GTFP.

Figure1. The theoretical framework of derivation

Comment 3: Referencing Issues: There are multiple issues with referencing numbers and formatting, which do not follow the journal’s referencing style and create confusion for the reader.

Response 3: Thank you for this helpful reminder. We acknowledge that the original version contained several inconsistencies in reference numbering and formatting that did not fully comply with the journal’s style requirements. In response, we have carefully reviewed and revised both the in-text citations and the reference list to ensure full alignment with the journal’s referencing guidelines. We apologize for any confusion this may have caused and hope that the revised version fully addresses your concern..

Comment 4: Missing Conclusion: The study lacks a conclusion section, which is crucial to summarize findings, contributions, and implications.

Response 4: Thank you for this very valuable and constructive comment. In response, we have substantially revised the Conclusion section to address your concern. Specifically, (1) we have clarified how the results relate to the theoretical frameworks adopted in the study, including the TOE framework, sustainable development theory, and innovation diffusion theory, and explicitly summarized the main theoretical contributions; (2) we have added a discussion of the study’s limitations and clarified the contextual boundaries of the findings, noting that the results are based on Chinese listed firms and may vary under different institutional or market conditions; (3) we have refined the policy and managerial implications by adopting a dual-structure format, separating government-level policy recommendations and firm-level management suggestions, and grounding them more concretely in institutional realities and implementation costs; and (4) we have articulated the study’s broader contributions to both theory development and economic policy practice in the field of digital transformation and green productivity. We sincerely hope that these improvements address your concern and enhance the clarity and impact of the paper. For your convenience, we have included the revised sections below.

7. Conclusion

This study systematically investigates the nexus between digital trade and corporate GTFP through a life cycle lens, employing panel data from Chinese A-share listed firms and 287 prefecture-level cities (2012–2022). Four pivotal findings emerge: First, digital trade exerts a robust positive impact on GTFP (β = 0.004, p < 0.05), predominantly mediated by technical efficiency improvements (GEC). This effect persists across robustness checks addressing endogeneity via instrumental variables and quasi-experimental designs. Second, the GTFP effects diverge across corporate maturity stages. Mature and declining firms prioritize efficiency gains (GEC: β = 0.006–0.008), while growth-phase firms exhibit aggregate productivity improvements (β = 0.011) without significant GEC/GTC differentiation. Technological lock-in and resource constraints in later stages amplify efficiency-focused strategies over innovation-driven approaches. Third, digital trade enhances GTFP through dual channels: fostering green technological innovation and optimizing supply chain coordination. Forth, Our analysis also highlights two layers of non-linear threshold effects: on the one hand, the marginal benefits of digital trade on GTFP vary across different stages of digital trade development, exhibiting nonlinear dynamics with diminishing returns at higher penetration levels; On the other hand,, institutional factors such as environ-mental regulation, intellectual property protection, and market integration further moder-ate this relationship, amplifying or constraining the impact of digital trade depending on their alignment. These findings underscore the importance of aligning digital trade strategies with supportive and adaptive institutional environments to realize their full sustainability potential. They also point to the need for phased, context-sensitive policy interventions that can foster complementary capabilities across different regions and firm types.

Based on these insights, we offer the following policy recommendations. First, governments should promote the coordinated development of digital trade and green industrial policies to create an environment that enables sustainable innovation. Environmental regulations should appropriately stimulate digital-driven green transformation while balancing compliance costs and innovation incentives. Strengthening IP protection frameworks and deepening market integration at domestic and international levels will further enhance digital trade's positive effects on firms' GTFP. From a managerial perspective, firms should actively integrate digital technologies into green innovation strategies and leverage digital platforms to drive sustainable product and process upgrades. Investing in advanced digital supply chain management is also essential to improving resource efficiency and environmental performance. Furthermore, firms should align their digital transformation initiatives with evolving regulatory landscapes and leverage cross-regional digital trade platforms strategically to expand markets for green products and services.

Despite its contributions, this study has several limitations. First, while our digital trade index captures city-level digital trade development, it does not fully reflect firm-specific digital trade intensity, which may introduce measurement bias. Second, our analysis is based on Chinese listed firms, which may limit the generalizability of the findings to other institutional contexts or small, nonlisted enterprises. Third, although we examined two key mechanisms—green innovation and supply chain management—other potential pathways require further investigation. Future research could address these limitations by developing more granular, firm-level digital trade indicators; conducting cross-country, comparative studies; and exploring additional mediating mechanisms. Additionally, longitudinal case studies could offer deeper insights into how digital trade influences GTFP over time.

Comment 5: No Limitations or Future Research: The manuscript omits a clear limitations and future studies section, which is a standard and important component of scholarly research.

Response 5: Thank you for this important suggestion. We fully acknowledge that the original version did not include a clearly defined section on limitations and future research. In the revised manuscript, we have added this component to the Conclusion (Section 7, final paragraph), where we explicitly discuss the study’s limitations and outline possible directions for future research. We hope this revision addresses your concern and aligns the manuscript with standard academic conventions. For your convenience, we have included the revised sections below.

Comment 6: Unclear Contribution in Introduction: The first contribution regarding the modification of the evaluation index system is not clearly stated or explained in the introduction, making it difficult to assess its relevance.

Response: We sincerely thank the reviewer for this valuable comment. We are fully aware that the original version of the introduction may not have clearly expounded on the various contributions of this research. Therefore, we revised the introduction part, sorted out the research gaps, and presented the research contributions, especially explaining the improvements in the digital trade evaluation index system, to make these contributions in a more organized and clear way. As for the construction of the digital trade evaluation index system, we have a more detailed explanation in Section 4.3.2. We sincerely hope that this revision can address the concerns of the reviewers and help convey the relevance and originality of the research more effectively. For your convenience, we have included the revised sections below.

1.Introduction(Paragraph 3 and Paragraph 4 of Section 1)

Although the relationship between the digital economy and sustainability is receiv-ing increasing attention, there are still some key gaps in the literature. First, the existing literature has examined the positive linear impact of digital trade on GTFP[11], and the U-shaped and inverted U-shaped nonlinear impacts of the digital economy on GTFP and TFP[12,13,14]. However, research on the nonlinear effects of digital trade on GTFP is lack-ing. Second, previous studies have not comprehensively examined the potential mediat-ing role of green technology R&D and supply chain management comprehensively, par-ticularly concerning their impact at different stages of the corporate life cycle. Third, re-garding external institutional factors, while existing research has demonstrated the posi-tive effects of government environmental regulations, innovation-encouraging policies and market integration on GTFP[15,16,17], the potential nonlinear and threshold effects of digital trade on GTFP when these factors are treated as threshold variables have not been widely analyzed.

In order to address this research gap, this paper makes the following innovative con-tributions. First, we developed a more refined measurement of digital trade development. We constructed a city-level Digital Trade Index that integrates digital financial inclusion metrics and captures the industrial foundation of regional digital trade systems. This pro-vides a more refined, context-sensitive assessment than prior province-level proxies [11]. Second, from the perspective of the enterprise life cycle, we analyze the impact of digital trade on the GTFP of enterprises at different stages of growth, maturity and decline, and reveal their heterogeneous characteristics, so as to provide more targeted theoretical guidance for enterprises to formulate reasonable trans-formation strategies in different development stages. Third, we select two unique perspectives, namely green technology efficiency and supply chain management efficiency, to study the transmission mecha-nism of digital trade on GTFP. Fourth, this paper investigates the non-linear multiple threshold effects of digital trade on firms’ GTFP, taking digital trade, environmental regu-latory constraints, intellectual property protection, and market integration as threshold variables, respectively, to explore the intrinsic law, provide policymakers with a scientific and prospective decision-making basis, and promote the simultaneous enhancement of digital trade and GTFP. China is a particularly relevant context for this research because its rapid development of digital trade, coupled with an ambitious green policy agenda, can provide empirical insights that can inform both emerging and developed economies..

Comment 7: Weak Literature Review: The literature review is limited to a few studies and lacks a comprehensive and critical discussion. There is insufficient combination to support hypothesis development.

Response 7: Thank you for this very helpful and constructive comment. We acknowledge that the original Literature Review section was limited in scope and did not provide a sufficiently comprehensive or critical discussion to support hypothesis development. In the revised manuscript, we have significantly improved this aspect in two key areas. First, in Section 2 (Theoretical Foundation), we have provided a more integrated conceptual background by incorporating Sustainable Development Theory, Innovation Diffusion Theory, and the Technology–Organization–Environment (TOE) framework. Second, in Section 3 (Literature Review and Hypothesis Development), we have expanded the coverage of relevant studies, organized the discussion more systematically, and more clearly linked prior research to the development of specific hypotheses. We now explicitly discuss how each mechanism and threshold variable relates to both digital trade and green total factor productivity (GTFP). We sincerely hope these improvements have strengthened the academic rigor of the literature review and addressed your concern. For your convenience, we have included the revised sections below.

Theoretical foundation

2.1. Sustainable development theory

2.2. Innovation diffusion theory and technology-organization-environment theory

Figure1. The theoretical framework of derivation

3. Literature Review and Hypothesis development

3.1. Digital trade and GTFP

The relationship between digital trade and GTFP is increasingly recognized as a critical research frontier in sustainable economic development. While both traditional and digital trade fundamentally involve the transfer of production factors, goods, and services to generate new spillover effects, digital trade demonstrates unique advantages through its platform-mediated exchanges. It enhances GTFP through dual mechanisms: improving operational efficiency while reducing output redundancies inherent in conventional trade practices.

The direct mechanisms operate through three primary channels: First, digital trade facilitates substantive reductions in production costs. Enterprises leveraging smart production systems and digital technologies can effectively minimize resource expenditure and emissions. Specifically, the deployment of big data analytics, cloud computing, and IoT solutions empowers dynamic process optimization, systematically lowering energy demands and waste outputs. This synergistic integration of pollution control and productivity enhancement represents a transformative efficiency paradigm central to GTFP advancement[29]. Second, digital trade reduces non-operational costs through market restructuring. The digital economy diminishes transaction costs while improving market access for green products. Lowering entry barriers and empowering SMEs to participate in global value chains fosters competitive ecosystems that incentivize sustainable innovation. This heightened market competition creates strategic imperatives for firms to differentiate through environmental stewardship, thereby generating endogenous momentum for GTFP improvement[30]. Third, digital trade establishes data-driven governance frameworks critical to GTFP enhancement. The inherent requirements for data transparency and algorithmic accountability in digital ecosystems compel firms to adopt lifecycle sustainability management[31]. Building on this analysis, we propose:

Hypothesis 1: The development of city digital trade exerts a positive influence on corporate GTFP.

3.2. The indirect impact of digital trade on GTFP

3.2.1. The role of green technology innovation

Digital trade enhances GTFP through two evolutionary mechanisms in green technology innovation(GTI): First, it catalyzes green technology innovation through knowledge spillover effects. The borderless nature of digital trade enables enterprises to access global green technology repositories and innovation ecosystems[32]. The advanced digital network facilitates instant knowledge sharing of sustainable best practices, leading to increased adoption of green technologies in emerging industries. Cross-border innovation clusters formed through cloud-based collaboration platforms allow firms to co-develop carbon capture systems and renewable energy solutions with international research institutions[25]. However, digital trade barriers (DTB) such as data localization requirements can fragment the innovation ecosystem and potentially reduce the efficiency of cross-border R&D[33]. Second, digital trade can enhance GTFP through green transformation. The green transformation would generate a cost premium[34], but digital trade could compress marginal costs through in-house artificial intelligence, realize a circular economy through IoT resource tracking in the supply chain, and promote green transformation by relying on the government's construction of various types of digital infrastructure (e.g., the broadband China strategy) and financial policies such as green credit[35]. Therefore, we propose the following hypothesis:

Hypothesis 2a: Digital trade development positively influences corporate GTFP enhancement through green technology innovation.

3.2.2. The role of supply chain management

Digital trade enhances GTFP through two synergistic supply chain mechanisms (SCM): First, it elevates supply chain transparency via digital traceability systems. Digital transformation improves the overall efficiency of the supply chain process, from the origin of goods to their delivery. It enables a smart supply chain and enhances competitive performance[36,37,38]. Empirical evidence from China indicates that supply chain digitization can promote urban resilience by improving GTFP [39]. Furthermore, supply chain digitization has been shown to positively impact GTFP at the corporate level, thereby contributing to environmental sustainability[40].

Next, it minimizes supply chain coordination costs through cyber-physical integration. digitalization within client companies can spill over to upstream suppliers, encouraging them to adopt cleaner technologies and more environmentally friendly practices. This impact is particularly evident in state-owned enterprises, where digital supply chain management has been shown to improve suppliers' GTFP by streamlining processes and minimizing emissions[41, 42]. Digital platforms and intelligent technologies have enhanced process efficiency, reduced resource waste, saved costs, strengthened collaboration, and supported green innovation, thereby promoting GTFP across the entire industry. Based on these findings, we propose the following hypothesis:

Hypothesis 2b: Digital trade development positively influences corporate GTFP enhancement through supply chain management.

3.3. Institutional Thresholds and Nonlinear Effects

Although digital trade can promote the improvement of GTFP, the magnitude and direction of these effects vary depending on the institutional environment. According to institutional theory[43], external institutional factors, such as regulatory frameworks, intellectual property protection, and market structure, fundamentally shape the incentives and constraints that firms face when adopting new technologies and sustainable practices. Previous research has shown that these institutional factors can produce nonlinear effects, or threshold dynamics, whereby the relationship between digital trade and GTFP varies under different institutional conditions[12,13,14].

3.3.1. Environmental Regulation

Environmental Regulation(ER) are an important external driver of green innovation and sustainable production. According to the Porter hypothesis[44], appropriately designed ER can stimulate innovation, enhance competitiveness, and improve environmental performance. However, the relationship between regulatory strictness and environmental productivity is often nonlinear [45]. Research indicates that moderate environmental regulations incentivize firms to adopt cleaner technologies and optimize production, thereby enhancing GTFP[46]. Conversely, overly lenient regulations may fail to provide sufficient incentives for green innovation, and overly strict regulations may impose excessive compliance costs, hindering productivity[45]. In the context of digital trade, the interaction with environmental regulations becomes more complex. Digital trade promotes green innovation and process optimization; however, the extent to which these benefits translate into GTFP improvements depends on environmental costs. Research indicates that high environmental taxes may inhibit corporate green innovation [47]. Under moderate regulatory pressure, firms are more likely to use digital tools to improve compliance and efficiency. However, under overly strict or overly lenient regimes, the potential benefits of digital trade may be suppressed. Therefore, we propose the following hypothesis:

Hypothesis 3a: The relationship between digital trade and GTFP is subject to a threshold effect based on the strictness of environmental regulation.

3.3.2. Intellectual Property Protection

Intellectual Property Protection(IPP) is another key institutional factor influencing corporate innovation incentives. A robust IPP regime stimulates innovation by encouraging disclosure and technology transfer [48]. A study on green innovation shows that IPP can stimulate innovation through intellectual property sharing strategies and accelerate the transition to sustainability[49]. However, the impact is nonlinear. Extremely weak intellectual property systems fail to incentivize innovation, and overly stringent intellectual property protection hinders the cross-company and cross-border dissemination of green technologies[50]. Therefore, we propose the following hypothesis:

Hypothesis 3b: The relationship between digital trade and GTFP is subject to a threshold effect based on the strength of intellectual property protection.

3.3.3. Market Integration

Market Integration(MI) is defined as the degree to which local markets are connected to broader national and global markets. MI plays a key role in shaping companies' growth and innovation opportunities. Higher levels of MI promote resource flows, knowledge exchange, and competitive pressures, which can accelerate the adoption of digital trade and its associated benefits [51]. Research indicates that, in highly integrated markets, firms can enhance GTFP by leveraging economies of scale, structural effects, and spillover effects [52]. However, as with other institutional factors, this relationship may exhibit threshold effects. In less integrated markets, moderate competition enhances innovation incentives. Conversely, intense competition can hinder innovation efforts when markets are overly integrated [53]. Therefore, we propose the following hypothesis:

Hypothesis 3c: The relationship between digital trade and GTFP is subject to a threshold effect based on the degree of market integration.

Comment 8: Methodological Gaps in Section 3.1: The authors fail to provide sufficient details about the sample size and data processing procedures, which weakens the reliability and replicability of the research.

Response 8: Thank you for identifying this critical omission. To enhance methodological transparency and improve the replicability of our analysis, we have revised the manuscript to include detailed explanations of the sample elimination criteria, the final sample size, and the data matching procedures. We hope this additional information addresses your concern and strengthens the reliability of the study. For your convenience, we have included the revised sections below.

4.1. Data sources

his study examined listed enterprises on the Shanghai and Shenzhen stock exchanges from 2012 to 2022. The study excluded ST and *ST enterprises, delisted enterprises, and enterprises with missing data during the sample period. To ensure data integrity, cities with a high number of missing values were excluded, resulting in the collection of digital trade data from 287 prefecture-level cities. Based on matching the digital trade data of prefecture-level cities with the registered locations of the listed enterprises, 1,625 enter-prises were selected as the research sample. Finally, 17,875 enterprise-annual unbalanced panel observation values were obtained. Digital trade data and other provincial and mu-nicipal data mainly come from the "China Urban Statistical Yearbook", the China Eco-nomic and Social Big Data Research Platform, the government work reports of various provinces, the Digital Finance Research Center of Peking University, and various public information of cities. The enterprise-related data mainly come from the CSMAR database, Wind database and CNRDS database.

Comment 9: Undefined Acronyms and Unclear Justification: The term “DT” is not explained when first introduced. Furthermore, the authors need to clarify why DT varies only by city and not by company, which currently lacks justification.

Response 9: Thank you for this valuable and constructive comment. In the revised manuscript, we have provided the full term “digital trade” when it first appears in Section 1, paragraph 2. In addition, we have clarified the rationale for using city-level digital trade indicators in Section 4.3.2 (Explanatory Variable: City Digital Trade), where we explain the data construction process and justify our approach. Specifically, our digital trade index is constructed at the city level due to data availability and consistency, as firm-level digital trade metrics are not accessible on a large scale. As a result, the digital trade variable varies by city rather than by firm. We hope this clarification resolves your concern. For your reference, the corresponding revised sections are provided below.

Introduction (paragraph 2)

Meanwhile, the use of data has become an integral part of business models[8]. Digital trade(DT) is defined as the provision of goods and services through digital means, with the help of digital technologies such as big data, cloud computing and the Internet of Things, digital trade breaks down the time and space barriers of traditional trade. It sig-nificantly reduces trade costs and greatly improves trade efficiency[9].

4.3.2. Explanatory variable: City digital trade

Due to the limited availability of corporate digital trade data, measuring digital trade at the corporate level is challenging. Therefore, this study proposes constructing a digital trade level at the city level and matching it with cities where listed companies are registered. This will allow us to investigate the impact of a city's digital trade level on the GTFP of companies located in that city. The construction of digital trade indicators in this study employs the entropy method, drawing on the work of Ma et al.[56] based on the WITS e-trade indicator system and referring to other literature on the research of evaluation index systems for the digital trade at the provincial level in China[57,11]. The existing indicator system was improved in two aspects: first, given the significant role of digital finance in enhancing digital trade efficiency[58], digital finance factors were introduced into the evaluation; second, the measurement of the regional digital trade industrial foundation was strengthened and improved. Based on the above research, the digital trade development evaluation indicator system at the city level in China is presented in Table 2.

Comment 10: Unvalidated Content Analysis Approach: While content analysis was used to measure greening transition, the authors fail to provide an appendix listing keywords or phrases used, and do not offer any validation of the measurement method.

Response 10: Thank you for identifying this critical omission. In the initial submission, we included an appendix listing the keywords used for measuring green transformation through content analysis. These keywords were derived from established methodological practices and widely adopted approaches in the literature on corporate green transformation. However, to avoid potential confusion and improve clarity, we have removed the appendix and instead cited relevant references that support the construction of this variable. We also acknowledge that the original description of the green transition variable was too brief. Based on your suggestion, we have expanded the explanation of this variable in the revised manuscript and provided a more detailed account of the measurement approaches used for other mechanism variables as well. We hope these revisions adequately address your concern. For your convenience, we have included the revised sections below.

4.3.4 Mechanism and threshold variables

The mechanism variables include green technology innovation and supply chain management. To confirm the robustness of green technological innovation as a mediating mechanism, this study incorporates a measure of enterprise greening transition (GreTrans) in addition to using the variable of firms filing green patents (EnvrPat). Green technological innovation is measured by the proportion of green patent applications filed by listed companies, or the ratio of green patents applied for by a company to the total number of patents applied for in a given year. (2) Greening transition refers to Kuo et al. [64]’s research on corporate green transition using textual information disclosed in annual reports. Based on relevant policy documents, 113 keywords related to corporate green transition were selected across five dimensions: promotional initiatives, strategic concepts, technological innovation, pollution control, and monitoring and management. Then, the frequency with which each keyword appeared in the text of the annual reports of listed companies was counted to create a green transformation keyword frequency count. The natural logarithm of this frequency plus one was used to characterize the company's green transformation.

To validate the role of supply chain management as a mediating mechanism, this study employs the variable of supply chain transparency (SCT) and adopts supply chain coordination costs (Recover) as an alternative mediating variable to verify robustness. (1) Supply chain transparency is represented by the number of large suppliers and customers whose names are explicitly disclosed by the firm; larger values indicate greater transparency. (2) Supply chain coordination cost reflects the supply chain's ability to stabilize after deviating from its original trajectory when hit by an external shock. Since measuring the cost of supply and demand coordination in the supply chain cooperation process directly is difficult, from the supply and demand perspective, when the supply chain is subject to external shocks, the original production and demand volumes of the upstream and downstream enterprises are affected. This causes an imbalance between supply and demand in the short term. Referring to the research of Shan et al.[65], this paper adopts the degree to which production fluctuations deviate from demand fluctuations to measure the accuracy of supply and demand matching in the enterprise supply chain. The calculation formula is shown in equations (4-5). Production represents the enterprise's output, Demand represents the enterprise's demand quantity (measured by the cost of sales), and Inventory represents the enterprise's net inventory value at the end of the year. Recover represents the deviation between supply and demand. If Recover is greater than one, it indicates that the fluctuation between supply at the beginning of the supply chain and demand at the end is relatively large and that the cost of supply chain coordination is high.

=+−	(4)
	(5)

Comment 11: Incomplete Table 1: Table 1 should include an additional column with recent references that justify the proxies selected for all variables.

Response 11: Thank you for identifying this critical omission. In line with your suggestion, we have revised Table 1 to include an additional column labeled “Data Source,” which provides the references and data sources used to justify the proxy selection for each variable. We hope this addition improves the transparency and academic rigor of the variable definitions. For your convenience, the revised table is included below.

Table 1. Construction of Green Total Factor Indicators for Enterprises

Variable		Measurement	Data source
Input	Labor	Number of employees	CSMAR (Referring to Wu et al. (2022))
	Capital	Net fixed assets	CSMAR (Referring to Wu et al. (2022))
	Energy	Urban industrial electricity consumption * number of persons employed in enterprise/number of persons employed in the city	Chinese City Statistical Yearbook (Referring to Wu et al. (2022))
Expected outputs	Business revenue	Annual revenue	CSMAR (Referring to Wu et al. (2022))
Non-expected outputs	Industrial sulfur dioxide (SO₂)	Urban industrial sulfur dioxide * number of persons employed in enterprise/number of persons employed in the city	Chinese City Statistical Yearbook (Referring to Wu et al. (2022))
	Industrial wastewater	Urban industrial wastewater * number of persons employed in enterprise/number of persons employed in the city
	Industrial smoke and dust emissions	Urban industrial smoke and dust emissions* number of persons employed in enterprise/number of persons employed in the city

Comment 12: Inconsistency in Descriptive Statistics: In Table 3, the mean for DT is reported as 0.575, while the maximum value is only 0.506, which is a major error and suggests serious issues with the dataset.

Response 12: Thank you very much for pointing out this important error. We would like to clarify that there is no issue with the underlying data. The reported mean value of DT (0.575) in Table 3 was a typographical error. The correct mean value is 0.0575, and the leading zero after the decimal point was mistakenly omitted. We have corrected this in the revised manuscript, and the mean value of DT now appears as 0.057. All values in Table 3 are now consistent and fall within the expected range. We appreciate your careful attention to this detail.

Comment 13: Assumed Contextual Knowledge: Section 4 assumes readers are familiar with digital trade and the specific Chinese institutional context, which separates international readers and limits accessibility.

Response 13: Thank you for this excellent and important comment. We fully acknowledge that the original version of Section 4 included contextual assumptions about digital trade development and institutional conditions in China, which may have limited accessibility for international readers. In the revised manuscript, we have addressed this concern in three ways.(1) In the Introduction and Theoretical Foundation sections, we now provide a clearer overview of the development of digital trade in China and the relevant institutional context. For example, we explain that China represents a particularly relevant setting due to the coexistence of rapid digital trade expansion and an ambitious green policy agenda, which may offer insights applicable to both emerging and developed economies. (2) In Section 4, we have added explanations to clarify context-specific decisions in variable selection. For instance, in Section 4.3.3 (Corporate Life Cycle), we explain why dividend payments—common in Western life cycle studies—are not adopted here: Chinese listed firms often pay low or no dividends, leading to a weak correlation with firm maturity, and thus limiting its applicability in this context. (3) In Section 5.4 (Robustness Check), we now clarify the rationale for excluding five autonomous regions (Inner Mongolia, Guangxi, Ningxia, Xinjiang, and Tibet). These regions typically have underdeveloped digital trade, lower data quality (especially on environmental and digital indicators), and are subject to stronger policy-driven cross-border activity, which may bias the estimation of market-based effects. This explanation has been added to ensure transparency for international readers.We sincerely hope that these revisions enhance the manuscript’s clarity and global accessibility. For your convenience, the revised table is included below.

Introduction (Paragraph 4)

China is a particularly relevant context for this research because its rapid development of digital trade, coupled with an ambitious green policy agenda, can provide empirical in-sights that can inform both emerging and developed economies.

4.3.3 Corporate life cycle

The reason why this study did not adopt variables such as dividend payments, which appear in life cycle literature, is primarily because Chinese listed companies generally prefer to pay few dividends or no dividends at all, resulting in a weak correlation between dividend payments and company growth. Therefore, this indicator is not suitable for use [63].

5.4. Robustness check

To ensure the reliability of the aforementioned conclusions, the robustness of the model was estimated in three aspects. First, samples were excluded from the five autono-mous regions (Inner Mongolia, Guangxi, Ningxia, Xinjiang, and Tibet). The level of digital trade development in these regions is relatively low, and the quality of the data, particu-larly the digitalization and environmental indicators at the enterprise level, is often in-complete or inconsistent. Furthermore, cross-border trade in these regions is often highly policy-driven rather than market-driven, which may lead to deviations in the estimation of market effects. Therefore, these samples might interfere with the estimation results and were removed for regression reanalysis.

Comment 14: Formatting and Structure: The manuscript suffers from inconsistent heading numbering, which should be corrected to meet the journal’s formatting standards.

Response 14: Thank you for this helpful reminder. In response to your comment, we carefully reviewed and corrected the heading numbering in accordance with the journal’s formatting requirements. The revised manuscript has been thoroughly checked to ensure consistency throughout. We confirm that all section and subsection headings now follow the journal’s formatting standards.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Editor and Authors,

Thank you for the opportunity to review this manuscript. The paper tackles an important question about digital trade and green productivity, which is a relevant topic. The authors should be commended for the effort. However, I find several critical issues that require attention.

Brief summary:

This study examines the effect of digital trade on firms' green total factor productivity using firm-level panel data. The authors apply fixed-effects regressions and some endogeneity controls to estimate the impact. The main result is that higher digital trade participation improves green productivity, particularly in later life cycle stages. While the topic is important and the idea is interesting, the current paper has serious methodological and clarity issues. Substantial revisions are needed to make the analysis sound and the writing clear.

Comments and Suggestions:

The study addresses a timely and relevant question. I like the focus on green productivity, which adds a novel aspect to the digital trade literature.
The theoretical foundation is weak. The authors should develop a stronger model linking digital trade to productivity and emissions, citing relevant theories.
There is no discussion of stationarity or dynamics in the panel data. If the data spans many years, unit root or cointegration issues might arise and should be tested.
Important factors (R&D, management practices, policy) are not fully controlled, which could drive both digitalization and productivity. So there could be omitted variable bias.
The life cycle perspective is mentioned but not clearly implemented. The concept of firm life cycle and how it is measured should be fully explained. . Important aspects like how firms are grouped by life cycle need clarification.
When introducing “Green Total Factor Productivity” (GTFP), please add a citation or brief definition. For example, reference a source defining GTFP and its measurement method so readers unfamiliar with it can understand.
Table 4: The stars for significance are listed, but the standard errors (or p-values) are not shown. Perhaps include p-values or at least clarify the significance levels in the notes.
Missing illustration: You claimed that the relationship between digital trade and GTFP is an inverted U. You didn’t have any figure or illustration to support your claim.
The paper lacks clarity in describing data and variables. For example, how is “digital trade” quantified at the firm level (binary, continuous, etc.)?
The results and discussion section narrative just repeats numbers from tables without interpretation. For instance, saying “the coefficient is positive” adds nothing. Explain what that actually means in practice.
The main numerical results (Table 4) are positive but very small (0.004 for DT). This is reported as significant and interpreted as “digital trade has a robust positive impact”. Yet no discussion on whether such a tiny increase in GTFP is meaningful.
The phrase “digital trade engagement” is unclear. Please define the variable formally (is it an index? share of revenue?).
I recommend reconsideration after major revisions. If the authors address all concerns thoroughly, the paper could become more scientifically sound and become suitable for acceptance.

Thank you for your work on this important topic. I can see how much effort went into this research. With some polishing and added details, this paper can really shine. I look forward to seeing the revised version.

Comments on the Quality of English Language

Sometimes you have “lifecycle” and other times “life cycle” so maybe you can decide if you want it as one word or two words.

Author Response

Comment 1. The study addresses a timely and relevant question. I like the focus on green productivity, which adds a novel aspect to the digital trade literature.

Response 1: We sincerely thank the reviewer for this positive and encouraging comment. We greatly appreciate your recognition of the study’s relevance and the value of focusing on green productivity within the digital trade literature. Your feedback motivates us to continue improving the quality and rigor of this research.

Comment 2: The theoretical foundation is weak. The authors should develop a stronger model linking digital trade to productivity and emissions, citing relevant theories.

Response 2: Thank you very much for this valuable comment. In response, we have added a chapter on Theoretical foundation to strengthen the theoretical foundation of the paper. Specifically, we have now more clearly articulated the mechanisms through which digital trade influences GTFP, drawing on sustainable development theory, innovation diffusion theory (Rogers, 2003), and the Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990). These theoretical perspectives now provide a more robust explanation of how digital trade facilitates green innovation, optimizes supply chain efficiency, and interacts with institutional factors to drive both productivity improvements and emissions reductions. We sincerely hope that these improvements address the reviewer’s concerns. For your convenience, we have included the revised sections below.

Theoretical foundation

2.1. Sustainable development theory

2.2. Innovation diffusion theory and technology-organization-environment theory

trade influences GTFP.

Figure1. The theoretical framework of derivation

Comment 3: There is no discussion of stationarity or dynamics in the panel data. If the data spans many years, unit root or cointegration issues might arise and should be tested.

Response 3: Thank you very much for your valuable suggestion. In response, we conducted panel unit root tests using the Harris-Tzavalis (HT) method (Harris & Tzavalis, 1999), which is well-suited for our short-panel data structure (large N, short T). The results, as reported in Section 4.5 (Table 4), confirm that all variables are stationary at levels across the sample period. This ensures that our empirical estimations are not subject to spurious regression concerns. For your convenience, we have included the revised sections below.

4.5. Panel Unit Root Tests

Since this study uses a balanced short-panel dataset, we performed panel unit root tests using the Harris-Tzavalis (HT) method[69], which is suitable for panels with large cross-sectional and short time series dimensions. The test results in Table 5 confirm that all variables are stationary at the level across the sample period. The stationarity of the variables mitigates concerns about spurious regression and supports the validity of subsequent fixed effects estimations.

Table 5. Stationarity test

Variable	Statistic	ADF-z	P-value	Conclusion
GTFP	-0.2118	-76.5720	0.0000	Smooth
DT	0.2016	-26.7110	0.0000	Smooth
EnvrPat	0.0173	-48.9372	0.0000	Smooth
GreTrans	-0.2029	-75.4980	0.0000	Smooth
SCT	0.2462	-21.3299	0.0000	Smooth
Recover	0.3842	-4.6868	0.0000	Smooth
ER	0.0585	-43.9771	0.0000	Smooth
IPP	0.2182	-24.7082	0.0000	Smooth
MI	0.1737	-30.0789	0.0000	Smooth
Size	-0.0226	-53.7496	0.0000	Smooth
Roa	0.1304	-35.3056	0.0000	Smooth
Lev	0.3850	-4.5915	0.0000	Smooth
Cashflow	0.3915	-3.8097	0.0001	Smooth
Growth	-0.1668	-71.1458	0.0000	Smooth
Soe	0.3688	-6.5485	0.0000	Smooth
Age	0.2212	-24.3485	0.0000	Smooth
Board	0.1923	-27.8348	0.0000	Smooth
Dual	0.2329	-22.9424	0.0000	Smooth
Top1	0.3895	-4.0469	0.0000	Smooth

Comment 4:Important factors (R&D, management practices, policy) are not fully controlled, which could drive both digitalization and productivity. So there could be omitted variable bias.

Response 4: Thank you for this insightful comment. We fully agree that factors such as R&D investment, management practices, and policy variables may influence both digitalization and productivity. However, in this study, first of all, we have selected the commonly used organizational control variables in enterprise research, such as Soe, Board, Dual, Top1, etc. Secondly, this paper does not take R&D variables as control variables in the baseline regression model because they represent the key mediating mechanism that assumes digital trade affects GTFP - green R&D innovation (Section 5.5); Finally, in terms of policy, we selected the pilot policy of the China Cross-border E-commerce Comprehensive Pilot Zone as a policy variable to address the endogeneity issue ( Section 5.3). If they are used as control variables in the benchmark model, there may be a risk of "over-control" and blurring the mechanism we need to determine. To address potential omitted variable bias, we adopted several approaches:(1)We employed instrumental variable (IV) estimation and quasi-experimental designs to mitigate endogeneity concerns (Section 4.6), helping to reduce the risk of confounding from unobserved factors. (2) We conducted extensive robustness checks with alternative specifications (Section 5.4), which confirmed the stability of our core findings.(3)Mechanism analyses (Section 5.5) explicitly model the roles of R&D and supply chain management, providing a more nuanced understanding of how digital trade influences GTFP through these channels.We sincerely thank the reviewer for highlighting this important issue.

Comment 5: The life cycle perspective is mentioned but not clearly implemented. The concept of firm life cycle and how it is measured should be fully explained. Important aspects like how firms are grouped by life cycle need clarification.

Response 5: We sincerely thank the reviewer for this valuable feedback. We agree that a clearer exposition of the firm life cycle framework was essential. To address this concern, we have added a dedicated subsection (Section 4.3.3 Corporate life cycle) in the revised manuscript, which systematically delineates the theoretical basis, measurement methodology, and grouping criteria. We ground our approach in established literature, citing Anthony & Ramesh (1992) and its adaptation for Chinese firms by Li et al. (2011). The life cycle stage is determined using a composite score based on four financial/growth indicators: Revenue growth rate, Retained earnings, Capital expenditures and Firm age. Each indicator is scored as High (3), Medium (2), or Low (1) (see Table 3). Further, We justify excluding dividend payments (common in Western studies) due to its weak correlation with growth in Chinese listed firms [70].Table 3 now explicitly details the criteria for assigning scores to each indicator across life cycle stages. These revisions provide a robust methodological foundation for our life cycle-based heterogeneity analysis. We believe this adequately clarifies the concept, measurement, and grouping process as requested. For your convenience, we have included the revised sections below.

4.3.3 Corporate life cycle

Existing research generally agrees that enterprise development goes through a life cy-cle process[59,60,61]. This study is based on the widely used corporate life cycle meas-urement method proposed by Anthony and Ramesh [62], with adjustments made to ac-count for the actual differences between industries in China. It draws on the approach adopted by Li et al.(2011)[ 63], which categorizes the business life cycle into growth, ma-turity, and decline stages based on a composite score of indicators such as revenue growth rate, retained earnings, capital expenditures, and firm age. The reason why this study did not adopt variables such as dividend payments, which appear in life cycle literature, is primarily because Chinese listed companies generally prefer to pay few dividends or no dividends at all, resulting in a weak correlation between dividend payments and compa-ny growth. Therefore, this indicator is not suitable for use [63]. In practical implementa-tion, industry differences were considered. The total sample was sorted by industry based on the total score of the four indicators. Each industry sample was then divided into three parts based on total score. The top third with the highest scores were classified as growth-stage companies; the bottom third with the lowest scores were classified as de-cline-stage companies; and the middle third were classified as mature-stage companies. Finally, the classification results for each industry were aggregated to obtain the classifi-cation results for the entire life cycle of all listed companies. The specific classification cri-teria are shown in Table 3.

Table 3. Criteria for dividing the stages of a company's life cycle

Variable	Revenue growth rate		Retained earnings		Capital expenditures		Firm age
Stage	Feature	Score	Feature	Score	Feature	Score	Feature	Score
Growth	High	3	High	3	High	3	High	3
Maturity	Medium	2	Medium	2	Medium	2	Medium	2
Decline	Low	1	Low	1	Low	1	Low	1

Comment 6: When introducing “Green Total Factor Productivity” (GTFP), please add a citation or brief definition. For example, reference a source defining GTFP and its measurement method so readers unfamiliar with it can understand.

Response 6: Thank you very much for this valuable comment. To ensure clarity for all readers, we have added both a concise conceptual definition and key methodological citations where GTFP is first introduced in the manuscript. These revisions:(1) Provide an accessible definition of GTFP’s core purpose (balancing economic/environmental outcomes); (2) Cite foundational literature on GTFP’s conceptual framing; (3) Cross-reference the detailed measurement methodology (Wu et al., 2022) in Section 4.3.1. We believe this addresses the reviewer’s request by anchoring GTFP in established literature while making its meaning clear to interdisciplinary audiences. For your convenience, we have included the revised sections below.

Introduction(Paragraph 1)

Concurrently, the concept of green development has become deeply embedded in public consciousness and policy agendas, positioning green total factor productivity (GTFP) as a critical metric for assessing firms' growth efficiency under environmental constraints[2, 3, 4]. Unlike traditional productivity indicators, GTFP considers undesirable outputs, such as carbon and pollutant emissions, to balance economic and ecological performance[5].

4.3.1 Explained variable: Corporate GTFP

The GTFP is considered an accurate indicator that takes into account both economic performance and the ecological environment [4]. It is considered an accurate indicator that takes into account both economic performance and the ecological environment. Drawing on the calculation method of Wu et al.[55] the SBM-ML model in data envelopment analy-sis is used to measure GTFP. Specifically, the model uses capital, labor, and energy con-sumption as input factors and divides output into expected output, represented by annual enterprise revenue, and unexpected output, represented by enterprise emissions of waste gas (SO₂), wastewater, and dust. The GTFP index can be decomposed into green technol-ogy efficiency (GEC) and green technology progress (GTC) through linear programming [55]. GEC stems from efficiency changes brought about by improvements in the produc-tion system, economies of scale, and experience accumulation. In contrast, GTC stems from efficiency changes resulting from improvements in production technology and pro-cess innovation. The measurement of input and output indicators of enterprise GTFP is shown in Table 1.

Table 1. Construction of Green Total Factor Indicators for Enterprises

Variable		Measurement	Data source
Input	Labor	Number of employees	CSMAR (Referring to Wu et al. (2022))
	Capital	Net fixed assets	CSMAR (Referring to Wu et al. (2022))
	Energy	Urban industrial electricity consumption * number of persons employed in enterprise/number of persons employed in the city	Chinese City Statistical Yearbook (Referring to Wu et al. (2022))
Expected outputs	Business revenue	Annual revenue	CSMAR (Referring to Wu et al. (2022))
Non-expected outputs	Industrial sulfur dioxide (SO₂)	Urban industrial sulfur dioxide * number of persons employed in enterprise/number of persons employed in the city	Chinese City Statistical Yearbook (Referring to Wu et al. (2022))
	Industrial wastewater	Urban industrial wastewater * number of persons employed in enterprise/number of persons employed in the city
	Industrial smoke and dust emissions	Urban industrial smoke and dust emissions* number of persons employed in enterprise/number of persons employed in the city

Comment 7: Table 4: The stars for significance are listed, but the standard errors (or p-values) are not shown. Perhaps include p-values or at least clarify the significance levels in the notes.

Response 7: We appreciate the reviewer’s meticulous attention to detail. We confirm that Table 4 and all statistical tables throughout the manuscript have been revised to address this point. Specifically:(1)Standard errors are now explicitly reported in parentheses below coefficient estimates in all tables. (2)Significance levels (10%, 5%, 1%) are clearly defined in every table note using asterisks (*, **, ***). These adjustments ensure full transparency and align with statistical reporting standards. Thank you for highlighting this oversight. For your convenience, we have included the revised sections below.

Note: The data in parentheses are robust standard errors, with “*”, “**”, and “***” indicating significance at the 10%, 5%, and 1% levels.

Comment 8: Missing illustration: You claimed that the relationship between digital trade and GTFP is an inverted U. You didn’t have any figure or illustration to support your claim.

Response: We thank the reviewer for highlighting this important omission. To visually substantiate the nonlinear relationship between digital trade and GTFP, we have added Figure 2 in Section 6 , titled:"The LR map corresponding to the first threshold and the second threshold estimate of the threshold variable". LR plots corresponding to the statistically significant thresholds identified for: Digital trade development stages, Institutional moderators: environmental regulation (ER), IP protection (IPP), and market integration (MI). Key support provided by Figure 2:(1)The LR plots visually confirm the inflection points where the relationship shifts direction. (2)The confidence intervals (dashed lines) validate threshold robustness. This addition provides the missing graphical evidence requested and strengthens the nonlinearity argument. For your convenience, we have included the revised sections below.

Figure 2. The LR map corresponding to the first threshold and the second threshold estimate of the threshold variable

Comment 9: The paper lacks clarity in describing data and variables. For example, how is “digital trade” quantified at the firm level (binary, continuous, etc.)?

Response 9: We thank the reviewer for this critical observation. To enhance clarity: Digital Trade (DT) is measured at the city level (not firm level) due to data constraints. As explicitly stated in Section 4.3.2. As for the relationship between the city digital trade index and firm: Each firm’s DT exposure is assigned based on its registered city’s annual DT index value. This city-firm matching enables analysis of how regional digital trade ecosystems affect firm productivity. For your convenience, we have included the revised sections below.

4.3.2. Explanatory variable: City digital trade

Due to the limited availability of corporate digital trade data, measuring digital trade at the corporate level is challenging. Therefore, this study proposes constructing a digital trade level at the city level and matching it with cities where listed companies are regis-tered. This will allow us to investigate the impact of a city's digital trade level on the GTFP of companies located in that city. The construction of digital trade indicators in this study employs the entropy method, drawing on the work of Ma et al.[56] based on the WITS e-trade indicator system and referring to other literature on the research of evaluation in-dex systems for the digital trade at the provincial level in China[57,11]. The existing indi-cator system was improved in two aspects: first, given the significant role of digital fi-nance in enhancing digital trade efficiency[58], digital finance factors were introduced in-to the evaluation; second, the measurement of the regional digital trade industrial founda-tion was strengthened and improved. Based on the above research, the digital trade de-velopment evaluation indicator system at the city level in China is presented in Table 2.

Comment 10: The results and discussion section narrative just repeats numbers from tables without interpretation. For instance, saying “the coefficient is positive” adds nothing. Explain what that actually means in practice.

Response 10: We sincerely thank the reviewer for this vital critique. We fully agree that merely reporting coefficients without contextual interpretation limits the scholarly value. In the revised manuscript, we have thoroughly restructured Section 5 (Results and Discussion) to translate statistical findings into economic/practical implications, moving beyond coefficient reporting. These revisions appear in Sections 5.1, 5.2, 5.5, 6.1 and 6.2. We now explicitly articulate why results matter—operationally, strategically, and socio-economically—addressing the core of the reviewer’s concern. For your convenience, we have included the revised sections below.

5. Results and discussion

5.1. Baseline regression results and discussion

As evidenced in Table 4 baseline regression results, digital trade exerts a statistically significant positive impact on corporate GTFP. After incorporating control variables, the regression coefficient for Digital Trade is 0.004, significant at the 5% level. This indicates that a one-unit increase in urban digital trade development corresponds to an average 0.004-unit improvement in corporate GTFP. These findings align with theoretical expectations, confirming digital trade’s catalytic role in enhancing green productivity. To disentangle transmission channels, we decompose GTFP into technical efficiency change (GEC) and technological progress change (GTC). Columns Table 4 (3)-(4) reveal that digital trade predominantly drives GTFP improvement through GEC elevation (β = 0.003, p < 0.05), while its effect on GTC remains statistically insignificant (β = 0.001, p > 0.1).

This divergence is theoretically consistent with the distinct characteristics of these two dimensions. GEC reflects a firm's ability to optimize the use of existing technologies and processes, thereby maximizing output efficiency within current production capabilities. In contrast, GTC requires innovations that shift frontiers and the adoption of new technologies, which typically entail longer R&D cycles, greater resource commitments, and uncertainty regarding commercialization outcomes. Thus, the observed channel-specific effects can be interpreted through the lens of temporal asymmetry in innovation diffusion: digital trade rapidly improves operational efficiency (GEC) by enhancing information transparency, reducing transaction costs, and improving coordination along the supply chain. In contrast, the effects on technological progress (GTC) may manifest more slowly as firms accumulate the necessary absorptive capacity and innovation experience. These findings align with the TOE framework's propositions [28], which posits that digital technology adoption initially drives organizational performance through efficiency gains and incremental innovations, with more radical technological advancements occurring over longer timeframes. Li et al.[70] and Wang et al. [71] also explain the positive impact of digital trade on GTFP in terms of efficiency in terms of digital technology's ability to optimize resource allocation, reduce environmental foot-prints and promote sustainable production processes. The current research results provide additional empirical evidence demonstrating how these effects are achieved through enhanced technical efficiency.

5.2. Heterogeneity effects across corporate life cycle stages

The responsiveness of GTFP to digital trade exhibits significant heterogeneity across different stages of the corporate life cycle, as reported in Table 7. Stratifying the sample by growth, maturity, and decline stages reveals distinct patterns in both the magnitude and mechanisms of he impact of digital trade on GTFP. These patterns align with the propositions of the TOE framework [28] and dynamic capability theory [72].

For firms in the growth stage, the coefficient for digital trade is positive and statistically significant, indicating that digital trade substantially enhances overall GTFP during this phase. However, neither technical efficiency change (GEC) nor technological progress change (GTC) channels are statistically significant. This finding echoes prior literature on absorptive capacity[73], suggesting that growth-stage firms, though highly dynamic, may lack the organizational routines necessary to fully internalize and exploit advanced digital technologies for green innovation. Moreover, consistent with innovation diffusion theory[24], firms in the early stages may focus their digital efforts on market expansion and customer acquisition, resulting in more indirect sustainability outcomes.. The positive aggregate GTFP gains likely stem from favorable external conditions, such as supportive policy environments and superior access to green subsidies [74], rather than from deliberate internal green innovation strategies.

During the maturity stage, the net effect of digital trade on GTFP is slightly negative, though not statistically significant. Decomposition analysis shows a positive impact on GEC and a negative, significant impact on GTC. This pattern suggests that mature firms primarily use digital trade to optimize existing processes through lean inventory, predictive maintenance, and energy efficiency. The negative GTC effect aligns with findings on technological path dependence and organizational inertia[75,76], where entrenched routines and legacy IT systems hinder firms' ability to adopt disruptive green technologies despite exposure to digital trade. These dynamics underscore the limitations of digital trade as a purely technological solution when organizational readiness for transformative innovation is lacking.

Among declining firms, digital trade has a positive and significant influence on GTFP, which is driven almost entirely by improvements in technical efficiency. Firms in this phase tend to adopt digital trade reactively and for survival purposes, prioritizing short-term gains such as resource pooling, platform-based cost savings, and tactical arbitrage. Consistent with crisis-driven innovation theory [77], these firms avoid long-term R&D commitments due to capital constraints and risk aversion, focusing digital strategies on immediate operational efficiencies. This further supports the argument that the organizational life cycle stage strongly shapes the scope and depth of digital trade's contribution to GTFP.

In sum, the heterogeneous impacts observed reflect a dynamic interplay of five key factors: (1) differential returns to efficiency (GEC) versus innovation (GTC); (2) capital constraints and debt pressures in mature and declining firms; (3) shifting strategic priorities across life cycle stages; (4) path dependency effects in mature firms; and (5) policy-driven advantages for growth-stage enterprises. These findings contribute to the literature on digital trade and sustainability by demonstrating that life cycle stage is a critical moderator of digital trade's contribution to GTFP. They also reinforce the idea that digital transformation strategies must be tailored to a firm's developmental context to achieve sustainable productivity gains.

5.5. Mechanism impact analysis

This section examines the underlying mechanisms through which digital trade enhances firms' GTFP. It focuses on two key channels: green technological innovation and supply chain management.

First, digital trade promotes GTFP by encouraging firm to research and develop green technologies, thereby accelerating their green transformation processes. As shown in Column (1) of Table 9 the coefficient of digital trade on firms' green R&D is 0.056 and is significant at the 5% level. This indicates that digital trade serves as a catalyst for firms’ investments in green innovation. This finding aligns with the innovation diffusion theory [26] and the TOE framework[28], which suggest that digital technologies lower information barriers, facilitate knowledge transfer, and stimulate firms’ technological upgrading.

When analyzing heterogeneity across life cycle stages (columns 2–4), the positive effect of digital trade on green innovation is most significant during the maturity stage. This pattern may reflect the greater responsiveness of mature firms to market signals and consumer demand for sustainable products. With their established resources and organizational capabilities, mature firms are better positioned to leverage digital trade platforms for green product innovation and process improvements [74].

Furthermore, digital trade contributes to a broader green transformation of firms. Column 5 of Table 9 shows that the coefficient of digital trade on firms' green transformation is 0.116, which is also significant at the 5% level. Examining the heterogeneous effects across life cycle stages (columns 6–8) reveals that the relationship evolves from negative or insignificant in the growth phase to positive in the maturity and decline stages. This evolution likely stems from varying strategic priorities: growth-stage firms typically prioritize expansion and market penetration, while mature and declining firms shift their focus toward sustainability and innovation as part of long-term competitiveness and survival strategies [76,77]. These results also support prior findings that green innovation and transformation significantly enhance GTFP and generate positive externalities for regional sustainability [40,46].

Overall, digital trade plays a critical role in strengthening the management of green supply chains. Firms in later life cycle stages typically face greater challenges in supply chain integration and experience more significant benefits from digitalization. Efficient supply chain management directly contributes to improvements in GTFP by optimizing resource utilization, reducing waste, and facilitating the adoption of green technologies [80]. During digital transformation, integrating supply chain processes enhances production efficiency and supports environmental performance, driving synergistic gains in economic and ecological dimensions.

Further non-linear impact explorations

6.1. Non-linear dynamic effects of digital trade

To further explore the nonlinear relationship between digital trade and firms' GTFP, this study uses a threshold regression model to evaluate the impact of various levels of digital trade development on GTFP outcomes. As shown in Table 11, significant single and double thresholds were identified at 0.010 and 0.1266, respectively. This indicates that the effects of digital trade vary across different stages of its penetration.

The regression estimates in Table 12, Column 1, show that the marginal impact of digital trade on gross territorial factor productivity (GTFP) increases nonlinearly. At low levels of digital trade (below 0.010), the effect on GTFP is negative (β=-1.186), possibly due to initial adjustment costs and capability mismatches. As digital trade penetration increases (0.010≤DT≤0.1266), the marginal impact becomes strongly positive (β=+0.526). This reflects the phase in which digital technologies effectively integrate with green innovation and resource optimization. Beyond the second threshold, when digital trade exceeds 0.1266, the positive impact remains significant (β=+0.398), though with diminishing marginal returns. This could be due to digital redundancy or organizational coordination challenges[81].

These findings suggest that the impact of digital trade on GTFP is nonlinear, with diminishing marginal returns at higher penetration levels. This highlights the importance of strategically timing digital adoption and complementing it with investments in green innovation capabilities to sustain productivity gains as digital trade matures, for both firms and policymakers.

6.2. Non-linear dynamic effects of the external environment

Columns (2) through (4) of Table 12 present the corresponding regression estimates. A pronounced nonlinear moderating effect is observed in all cases: favorable institutional conditions significantly enhance the positive impact of digital trade on GTFP compared to the benchmark model in column 1.

When environmental regulation is the threshold variable, the impact of digital trade on GTFP is nonlinear, growing at first and subsequently declining. At low levels of regulation (below 0.0058), digital trade has a modest positive effect on GTFP. As regulation strengthens (0.0058≤ER≤0.0077), the positive impact peaks, aligning with the Porter Hypothesis [44], which suggests that well-designed legislation can promote green innovation and productivity. However, when regulation grows more severe (ER>0.0077), the marginal advantage of digital trade declines, most likely due to resource constraints and compliance obligations that hinder enterprises from fully using digital capabilities. These findings show that moderate, market-friendly regulation works together with digital trade to optimize GTFP gains.

When IP protection is used as the threshold variable, the connection follows a "N-shaped" nonlinear pattern. At low IP protection levels (<0.0607), digital trade has a positive impact on GTFP. However, at intermediate levels (0.0607≤IP≤0.0924), the effect becomes negative, possibly due to adjustment costs, reduced knowledge spillovers, or limited technological diffusion under stricter IP regimes [48]. At higher levels of intellectual property protection (> 0.0924), the beneficial effect returns, as firms benefit from secure innovation environments and more incentives to invest in green technologies. These findings demonstrate that, while robust intellectual property protection is ultimately helpful, transitional phases may present temporary barriers to green innovation spread.

When market integration is used as the threshold variable, the connection repeats itself, growing and then reducing. At lower market integration levels (<15.385), digital trade increases GTFP by creating new market channels and facilitating access to green technologies. As market integration increases (15.385≤MI≤19.153), the favorable impact grows due to increased network effects and economies of scale[51]. However, at the upper threshold (> 19.153), marginal gains are marginally reduced, possibly due to market saturation or competitive crowding, which restricts further productivity advances.

Taken together, these findings demonstrate that external institutional environments have a significant impact on how digital trade influences GTFP. Moderate environmental regulation, strong IP protection, and optimal market integration all help to amplify the productivity-boosting effects of digital trade. Policymakers should therefore take a phased and adaptive approach, designing balanced regulatory frameworks, strengthening IP regimes while encouraging knowledge diffusion, and facilitating cross-regional market integration—all of which can help sustain the momentum of digital-driven green productivity growth.

Comment 11: The main numerical results (Table 4) are positive but very small (0.004 for DT). This is reported as significant and interpreted as “digital trade has a robust positive impact”. Yet no discussion on whether such a tiny increase in GTFP is meaningful.

Response 11: We thank the reviewer for this insightful critique regarding practical significance. We acknowledge the coefficient magnitude (β=0.004) appears modest at face value and have added nuanced interpretation in Section 5.1 to contextualize its importance: “While numerically modest, this effect is economically meaningful when considering the cumulative and compounding nature of productivity gains in highly competitive industrial environments.” Secondly, I rigorously referred to the studies on the impact of GTFP in other articles. In the article of Wu et al.(2022)[1], the regression coefficients regarding the impact of the technological gap on GTEC and GTC (the subdivision of the GTFP indicator) were also relatively small, with coefficients such as 0.00340 and 0.0037. It can be proved that the empirical results regarding GTFP allow the existence of micro-coefficients.

Comment 12: The phrase “digital trade engagement” is unclear. Please define the variable formally (is it an index? share of revenue?).

Response 12: Thanks for the corrections. Perhaps due to translation errors, there is no discussion on “Digital Trade engagement” in the article, only the measurement of the development level of Digital Trade. The wrong words have now been deleted.

Comment 13: Comments on the Quality of English Language

The writing contains frequent errors that make the manuscript hard to follow. This paper would benefit from some extensive editing. Many sentences are confusing due to typos, grammatical errors, incomplete sentences, and missing words.Sometimes you have “lifecycle” and other times “life cycle” so maybe you can decide if you want it as one word or two words.

Response 13: Thank you very much for the corrections of the reviewers. We have reorganized the entire text and clearly defined the writing of all life cycles as “life cycle”. Other potential spelling and grammar problems were also corrected at the same time.

[1] Wu J., Xia Q., Li Z. Green innovation and enterprise green total factor productivity at a micro level: A perspective of technical distance. Journal of Cleaner Production, 2022, 344: 131070.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Authors

I found your article very interesting and am grateful for the opportunity to read it. I found the subject of the research fascinating, and the results provide a wealth of new information and possibilities for further analysis.

I just have a few suggestions to improve the clarity of your article.

Your article is a technically advanced empirical study, conducted on a large dataset and using sophisticated econometric models. It is clear that you have considerable methodological competence and the ability to process data accurately, even at the technical level. However, the main issue with the article is the lack of in-depth interpretation of the results and the failure to contextualise the study.

Although the empirical research is conducted with great precision, it seems to exist 'for its own sake' – it does not answer a clearly defined research question that would justify its conduct. The article does not convince the reader that the questions asked are relevant or what they contribute to the literature. Nor does it explain how the results can be interpreted in causal, systemic or institutional terms. Consequently, the text reads like a statistical report that has not progressed beyond the conceptualisation stage.

The biggest problem with the article is the lack of a discussion of the results. In scientific publications, this serves as a bridge between data and theory. You merely confirm statistical hypotheses without attempting to explain the found relationships in greater depth. You do not ask questions about alternative mechanisms, nor do you relate the results to existing theories of environmental economics, digital transformation or innovation.

Importantly, the structure of the text suggests that this section may have been planned: point 4.6 is followed by point 5.2, which indicates that part of the text may be missing, perhaps an interpretative section. If this is not an editorial error, it suggests that the authors did not finish their work.

A second issue is that, despite its considerable volume and wide range of sources, the literature review is superficial. It is descriptive in structure, with no attempt at conceptualisation, critical synthesis of positions, or organisation of research trends. In this section, gaps in the literature are not identified, a theoretical framework is not defined, and the cited publications serve more as illustrations than arguments. There is a definite lack of references to classic concepts in sustainable development theory, innovation diffusion, and business theory. Consequently, the review fails to fulfil its key role of creating a foundation on which the research is based.

The third element is the 'Conclusions', which also suffer from a lack of cognitive reflection. They do not attempt to conceptualise the obtained results, nor do they refer to the study's limitations or the context in which the obtained relationships may (or may not) apply. There are also no practical recommendations; the ones proposed are vague, technocratic and insufficiently grounded in institutional or cost realities. Consequently, the article does not make a lasting contribution to theory development or economic policy practice.

Summary:

While the article excels at operationalising and analysing data, it fails to fulfil the fundamental purpose of scientific research: answering an important research question and contextualising the results. Although the research was conducted, its cognitive — and therefore scientific — value remains incomplete. In its current form, the text needs to be supplemented with:

- a clearly formulated research problem;

- an in-depth discussion of the results;

- a coherent theoretical framework;

- realistic recommendations and an indication of limitations.

Without these elements, the article does not exploit the full potential of its empirical layer and remains fragmentary, unfit for publication in its current form. This change does not require modification of the data or methods, but rather a thorough refinement of the analytical and interpretative layers – the layers that make the study truly scientific.

Despite my critical comments, the article has great potential, and the research is sound. As a side note, you could avoid using excessive jargon, as this makes the article difficult to read. Remember that, apart from publication, the purpose of the article is its citability, among other things. No one will cite your research if it does not provide interpretations, results and conclusions.

Good luck!

Author Response

Reviewer #3：

Comments and Suggestions:

Comment 1: Your article is a technically advanced empirical study, conducted on a large dataset and using sophisticated econometric models. It is clear that you have considerable methodological competence and the ability to process data accurately, even at the technical level. However, the main issue with the article is the lack of in-depth interpretation of the results and the failure to contextualise the study.

Response1 : We sincerely thank the reviewer for the encouraging comments regarding the methodological rigor of our study. We also fully acknowledge the concern regarding the depth of result interpretation and contextualization. In response, we have substantially enriched the interpretation of the empirical results in Sections 5.1, 5.2, 5.5, 6.1 and 6.2., providing deeper theoretical explanations(including the TOE framework, innovation diffusion theory, and dynamic capability theory) and engaging more directly with relevant literature. For your convenience, we have included the revised sections below.

5. Results and discussion

5.1. Baseline regression results and discussion

5.2. Heterogeneity effects across corporate life cycle stages

5.5. Mechanism impact analysis

This section examines the underlying mechanisms through which digital trade enhances firms' GTFP. It focuses on two key channels: green technological innovation and supply chain management.

Further non-linear impact explorations

6.1. Non-linear dynamic effects of digital trade

6.2. Non-linear dynamic effects of the external environment

Comment 2: Although the empirical research is conducted with great precision, it seems to exist 'for its own sake' – it does not answer a clearly defined research question that would justify its conduct. The article does not convince the reader that the questions asked are relevant or what they contribute to the literature. Nor does it explain how the results can be interpreted in causal, systemic or institutional terms. Consequently, the text reads like a statistical report that has not progressed beyond the conceptualisation stage.

Response 2: We deeply appreciate the reviewer’s incisive critique, which highlights gaps in articulating our study’s scholarly significance and theoretical framing. We have undertaken major revisions to strengthen these dimensions, as detailed below: (1)Sharpening Research Questions & Scholarly Relevance: Revised Introduction now explicitly frames three research gaps. (2) Contribution clarity: The revised introduction also clearly mentions the four contributions of the article. （3）Theoretical Anchoring in Causal/Systemic Frameworks: we have added a dedicated section on the theoretical foundation to enhance the conceptual grounding of the paper. Specifically, we now explain more clearly the mechanisms through which digital trade influences green total factor productivity (GTFP), drawing on sustainable development theory, innovation diffusion theory (Rogers, 2003), and the Technology–Organization–Environment (TOE) framework (Tornatzky & Fleischer, 1990). These theoretical perspectives provide a more rigorous explanation of how digital trade can promote green innovation, improve supply chain efficiency, and interact with institutional conditions to jointly advance productivity and environmental performance. We sincerely hope that these revisions adequately address your concern. For your convenience, we have included the revised sections below.

1.Introduction(Paragraph 3 and Paragraph 4 of Section 1)

Theoretical foundation

2.1. Sustainable development theory

2.2. Innovation diffusion theory and technology-organization-environment theory

Figure1. The theoretical framework of derivation

Comment 3:The biggest problem with the article is the lack of a discussion of the results. In scientific publications, this serves as a bridge between data and theory. You merely confirm statistical hypotheses without attempting to explain the found relationships in greater depth. You do not ask questions about alternative mechanisms, nor do you relate the results to existing theories of environmental economics, digital transformation or innovation.

Response 3: We sincerely appreciate the reviewer’s insightful comment regarding the importance of in-depth discussion and theoretical integration. n the revised manuscript, we have substantially strengthened the discussion of the empirical results in Sections 5.1, 5.2, 5.5, 6.1 and 6.2., explicitly linking the findings to the theoretical frameworks of sustainable development, innovation diffusion, and the TOE model. Furthermore, we have also explicitly discussed alternative mechanisms in the Mechanism analysis. As mentioned in 4.3.4 Mechanism and threshold variables, enterprise greening transition (GreTrans) is a surrogate variable for firms filing green patents (EnvrPat). supply chain coordination costs (Recover) is an alternative variable of supply chain transparency (SCT) to ensure the robustness of the mediation mechanism. We sincerely hope that these revisions now better address the reviewer’s concerns and improve the theoretical depth of the manuscript. For your convenience, we have included the revised sections 4.3.4 below. (Section 5.1, 5.2, 5.5, 6.1 and 6.2. are displayed in comment 1)

4.3.4 Mechanism and threshold variables

=+−	(4)
	(5)

Comments 4:Importantly, the structure of the text suggests that this section may have been planned: point 4.6 is followed by point 5.2, which indicates that part of the text may be missing, perhaps an interpretative section. If this is not an editorial error, it suggests that the authors did not finish their work.

Response 4: Thank you very much for this helpful observation. This was indeed a formatting error in the numbering of the sections, which occurred during manuscript preparation. The original manuscript did indeed follow from “4.5.1” to “5.2” immediately because “5.2” was supposed to be “4.5.2”, but “4” was mistakenly deleted. No content is missing, the full text, including the interpretative sections and all relevant analyses, is complete. We have corrected the section numbering in the revised version of the manuscript to ensure clarity and completeness. We apologize for any confusion this may have caused and thank the reviewer for pointing it out.

Comment 5: A second issue is that, despite its considerable volume and wide range of sources, the literature review is superficial. It is descriptive in structure, with no attempt at conceptualisation, critical synthesis of positions, or organisation of research trends. In this section, gaps in the literature are not identified, a theoretical framework is not defined, and the cited publications serve more as illustrations than arguments. There is a definite lack of references to classic concepts in sustainable development theory, innovation diffusion, and business theory. Consequently, the review fails to fulfil its key role of creating a foundation on which the research is based.

Response 5: We sincerely thank the reviewer for this very constructive and helpful comment. We fully acknowledge that the original literature review was somewhat descriptive and did not sufficiently highlight theoretical frameworks or research gaps. In response, we have significantly revised both the Theoretical Foundation (Section 2) and Literature review and Hypothesis Development (Section 3) integrates more literature viewpoints and provides a more complete conceptual model. The revised literature review now presents a clearer synthesis of existing research, and situates our study’s contribution within the broader theoretical context. We have also made sure to reference classic works in sustainable development, innovation diffusion, and digital transformation. We sincerely hope that these revisions have strengthened the theoretical foundation of the paper and addressed the reviewer’s concerns. For your convenience, we have included the revised Section 3 below(Section 2 is displayed in comment 2).

3. Literature Review and Hypothesis development

3.1. Digital trade and GTFP

Hypothesis 1: The development of city digital trade exerts a positive influence on corporate GTFP.

3.2. The indirect impact of digital trade on GTFP

3.2.1. The role of green technology innovation

Hypothesis 2a: Digital trade development positively influences corporate GTFP enhancement through green technology innovation.

3.2.2. The role of supply chain management

Hypothesis 2b: Digital trade development positively influences corporate GTFP enhancement through supply chain management.

3.3. Institutional Thresholds and Nonlinear Effects

3.3.1. Environmental Regulation

Hypothesis 3a: The relationship between digital trade and GTFP is subject to a threshold effect based on the strictness of environmental regulation.

3.3.2. Intellectual Property Protection

Hypothesis 3b: The relationship between digital trade and GTFP is subject to a threshold effect based on the strength of intellectual property protection.

3.3.3. Market Integration

Hypothesis 3c: The relationship between digital trade and GTFP is subject to a threshold effect based on the degree of market integration.

Comment 6: The third element is the 'Conclusions', which also suffer from a lack of cognitive reflection. They do not attempt to conceptualise the obtained results, nor do they refer to the study's limitations or the context in which the obtained relationships may (or may not) apply. There are also no practical recommendations; the ones proposed are vague, technocratic and insufficiently grounded in institutional or cost realities. Consequently, the article does not make a lasting contribution to theory development or economic policy practice.

Response 6: We sincerely thank the reviewer for this very valuable and constructive comment. In response, we have substantially revised the Conclusion section to address these concerns. Specifically: (1) The policy and managerial implications have been revised and now presented as a dual-structure (government policy recommendations and firm-level managerial recommendations), grounded more concretely in institutional realities and implementation costs. (2) We have added a discussion of the study’s limitations and clarified the contextual boundaries of the findings. (3) We discussed the future research directions of digital trade and GTFP. We sincerely hope that these improvements better address the reviewer’s concerns and enhance the value and impact of the paper. For your convenience, we have included the revised sections below.

7. Conclusion

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Sustainability

Subject: Referee Report for Manuscript ID sustainability-3696225

Title: How Does Digital Trade Affect Firm’s Green Total Factor Productivity? A Life Cycle Perspective

The paper examines the impact of digital trade on firm-level green total factor productivity (GTFP) using panel data on Chinese A-share listed companies from 2012–2022. The authors define GTFP as a productivity measure adjusted for environmental factors (incorporating carbon emissions/pollution as “undesirable outputs”) to capture both economic and ecological efficiency. They posit that greater engagement in digital trade, broadly referring to the use of digital platforms and ICT for commerce, can improve a firm’s GTFP by spurring efficiency gains and innovation. A key contribution is the consideration of a firm’s life cycle stage (e.g., growth, maturity, decline) as a moderating factor in this relationship. The paper also explores two mechanisms through which digital trade may influence GTFP: (1) green technological innovation (e.g., R&D in clean tech, green patents) and (2) supply chain coordination (e.g,. digital integration with suppliers/customers to reduce waste and inefficiency). Finally, the authors investigate whether the digital trade–GTFP relationship is non-linear and subject to threshold effects based on external conditions, specifically the stringency of environmental regulation, strength of intellectual property (IP) protection, and degree of market integration in the firm’s operating environment.

In terms of findings, the paper reports several interesting results.

First, digital trade has a statistically significant positive effect on firms’ GTFP on average. This suggests that firms leveraging digital platforms for trade tend to achieve higher productivity while reducing their environmental impact, consistent with arguments that digitalization can optimize resource allocation and spur green innovation.

Second, the impact of digital trade on GTFP varies over the firm’s life cycle: the productivity gains are most pronounced in the growth phase of a firm’s development, whereas mature and declining firms see more modest improvements primarily through efficiency (rather than innovation) gains. This heterogeneity is attributed to younger firms more readily adopting new technology and business models, whereas older firms face “technological lock-in” and resource constraints that make digital adoption yield only incremental efficiency improvements.

Third, the analysis finds evidence for dual channels of impact – greater digital trade is associated with increased green innovation and improved supply chain coordination, which in turn both contribute to higher GTFP. For example, firms engaging in digital trade show higher green patent counts and better integration with suppliers (leading to less waste and energy use), supporting these as mechanisms.

Fourth, the relationship between digital trade intensity and GTFP appears to be non-linear (inverted U-shaped). The authors identify an optimal range of digital trade (around a digital trade index value of 0.12) beyond which the marginal benefits to GTFP diminish and can even become negative. This pattern likely reflects that initial investments in digital infrastructure yield efficiency gains, but after a point, diminishing returns or management challenges set in.

Finally, the external context matters: stronger environmental regulations, well-defined IP rights, and greater market integration all raise the “optimal” level of digital trade and amplify its positive effect on GTFP (up to certain thresholds). For instance, the paper finds that when environmental policy is neither too lax nor too stringent (within an index range of roughly 4.2–5.8), digital trade’s benefits to GTFP are maximized – very weak regulation fails to incentivize green efforts, while overly stringent regulation may impose compliance costs that dampen productivity. Similar threshold effects are noted for IP protection (with an optimal range around 6.0–7.5) and market integration (around 0.7–0.9), suggesting that a supportive institutional environment is needed to fully realize digital trade’s sustainability benefits.

Overall, the manuscript’s core message is that digital trade can be a catalyst for sustainable productivity growth at the firm level, but its effectiveness depends on where the firm is in its development cycle and on complementary policies/institutions.

I have several concerns about the methodology and interpretation of results.

Identification Strategy and Endogeneity: establishing a credible causal link from digital trade to GTFP. Firms that engage in digital trade might systematically differ from those that do not – for example, they could be larger, more innovative, or have better management, which itself could drive higher GTFP. The manuscript mentions using instrumental variables (IV) and a “quasi-experimental design” to address endogeneity, but these methods are not described with sufficient clarity. Please elaborate here.

Measurement of Key Variables (Digital Trade and GTFP): There is some ambiguity in how the central variables are measured. Digital trade at the firm level is a somewhat abstract concept – the authors need to clearly define their “digital trade index” (DTI). Does it measure the share of a firm’s sales conducted online, usage of e-commerce platforms, or perhaps an index of digital technology adoption in operations? The data source and construction of this variable should be explained in detail. Likewise, green TFP computation should be clarified. The paper seems to use a variant of data envelopment analysis (DEA) or the Malmquist–Luenberger index to compute GTFP, incorporating firm-level emissions data. However, firm-level environmental data for Chinese companies can be sparse. Are emissions or energy usage observed for each firm, or are industry-level proxies used? A methodological concern is whether measurement error in GTFP or digital trade could bias the results. I recommend that the authors provide a subsection detailing the GTFP calculation (e.g., which inputs and outputs are used in the TFP model, how “green output” or pollution is accounted for) and how they obtained firm-specific environmental metrics. If any strong assumptions are involved (such as assuming all firms in an industry have proportional emissions), that should be stated. Clear definitions will also help readers assess the reliability of the findings.

Mechanism Channels – Green Innovation and Supply Chain Coordination: The paper highlights green technological innovation and supply chain coordination as mechanisms, which is a strong point, but the treatment of these mechanisms raises a few concerns. The measurement of “green innovation” should be clarified – presumably, it’s something like the number of green patents or R&D expenditures focused on environmental projects. If it is patents, are these all patents in environment-related classes, and is the data from a particular source (e.g., patent office records)? If it is R&D, how do we distinguish green R&D from general R&D? Similar questions apply to “supply chain coordination” – an interesting but vague construct. The authors might be using a proxy such as whether the firm has an integrated IT system for the supply chain or the number of strategic supplier partnerships. This needs to be explained.

Connection to Finance Literature: The manuscript could do more to tie its findings into the broader finance dialogue. For example, if digital trade indeed improves firms’ green performance, one might expect capital markets to reward such firms (given evidence that investors value sustainability and direct funds towards greener firms. Citing a few key papers in this domain would show awareness – e.g., Hartzmark and Sussman (2019) show that mutual fund flows respond to sustainability ratings, and recent work finds risk disclosure regulations alter investor behavior (Mugerman et al., 2022). Bringing this perspective in the introduction or conclusion would underscore why improving GTFP via digital trade is not just environmentally and operationally beneficial, but potentially financially as well. This would enrich the paper’s appeal to finance-focused readers.

References:

Hartzmark, S. M., & Sussman, A. B. (2019). Do investors value sustainability? A natural experiment examining ranking and fund flows. Journal of Finance, 74(6), 2789–2837. https://doi.org/10.1111/jofi.12841
Mugerman, Y., Steinberg, N., & Wiener, Z. (2022). The exclamation mark of Cain: Risk salience and mutual fund flows. Journal of Banking & Finance, 134, 106332. https://doi.org/10.1016/j.jbankfin.2021.106332

Author Response

Reviewer #4：

Comments and Suggestions:

Comment 1: Identification Strategy and Endogeneity: establishing a credible causal link from digital trade to GTFP. Firms that engage in digital trade might systematically differ from those that do not – for example, they could be larger, more innovative, or have better management, which itself could drive higher GTFP. The manuscript mentions using instrumental variables (IV) and a “quasi-experimental design” to address endogeneity, but these methods are not described with sufficient clarity. Please elaborate here.

Response 1: We sincerely thank the reviewer for raising this very important issue. We fully acknowledge the potential endogeneity concerns in estimating the causal impact of digital trade on GTFP, particularly due to the possibility that more innovative or better-managed firms may self-select into digital trade adoption. Our identification strategy employs two complementary approaches to mitigate this bias: (1)Instrumental Variable (IV) approach: We adopt a Bartik-style instrumental variable (IV). This method, originally proposed by Bartik[78], has been widely applied in empirical re-search where direct firm-level instruments are unavailable. Specifically, the instrument is constructed by interacting a firm’s lagged city-level digital trade index (capturing the ini-tial “exposure” to digital infrastructure) with the national-level growth rate of digital trade (representing an exogenous macro-level “shock”). This interaction generates plausibly exogenous variation in local digital trade development that is not directly driven by firm-level productivity shocks. The rationale is that while national trends in digital trade evolve due to broader technological and policy dynamics, the extent to which these trends affect any given firm depends on its initial regional exposure to digital trade conditions. This approach offers a credible identification strategy when firm-level digitalization measures are not directly observable. (2)Quasi-experimental design: We further exploit staggered roll-outs of the staggered establishment of China’s Cross-Border E-Commerce Comprehensive Pilot Zones (CBEC Pilot Zones), which provide exogenous policy shocks to local digital trade environments, creating temporal and spatial variation. We apply a difference-in-differences (DID) framework comparing treated and control firms before and after pilot implementation. This helps control for unobserved time-invariant heterogeneity and mitigate reverse causality concerns. We fully recognize that the introduction of instrumental variables and DID experiments in the original manuscript is not clear enough, so detailed supplements have been made in Section 5.4 For your convenience, we have included the revised sections below.

5.4. Robustness check

To ensure the reliability of the aforementioned conclusions, the robustness of the model was estimated in three aspects. First, samples were excluded from the five autonomous regions (Inner Mongolia, Guangxi, Ningxia, Xinjiang, and Tibet). The level of digital trade development in these regions is relatively low, and the quality of the data, particularly the digitalization and environmental indicators at the enterprise level, is often incomplete or inconsistent. Furthermore, cross-border trade in these regions is often highly policy-driven rather than market-driven, which may lead to deviations in the estimation of market effects. Therefore, these samples might interfere with the estimation results and were removed for regression reanalysis. Second, add provincial-level clustering. Multilevel clustering captures spatial correlations and enhances regression robustness. It was added based on individual and urban clustering. Finally, use the propensity score matching (PSM) method. This method was adopted for robustness tests to reduce estimation bias interference. Specifically, 1:1 nearest neighbor matching was used to re-estimate the model. As shown in columns 5 through 7 of Table 8, the regression results remained significantly positive after these robustness tests, indicating the validity of the basic conclusions in this paper.

Comment 2:Measurement of Key Variables (Digital Trade and GTFP): There is some ambiguity in how the central variables are measured. Digital trade at the firm level is a somewhat abstract concept – the authors need to clearly define their “digital trade index” (DTI). Does it measure the share of a firm’s sales conducted online, usage of e-commerce platforms, or perhaps an index of digital technology adoption in operations? The data source and construction of this variable should be explained in detail. Likewise, green TFP computation should be clarified. The paper seems to use a variant of data envelopment analysis (DEA) or the Malmquist–Luenberger index to compute GTFP, incorporating firm-level emissions data. However, firm-level environmental data for Chinese companies can be sparse. Are emissions or energy usage observed for each firm, or are industry-level proxies used? A methodological concern is whether measurement error in GTFP or digital trade could bias the results. I recommend that the authors provide a subsection detailing the GTFP calculation (e.g., which inputs and outputs are used in the TFP model, how “green output” or pollution is accounted for) and how they obtained firm-specific environmental metrics. If any strong assumptions are involved (such as assuming all firms in an industry have proportional emissions), that should be stated. Clear definitions will also help readers assess the reliability of the findings.

Response 2: We sincerely thank the reviewer for this important and very constructive comment. We fully agree that clear and precise definitions of the key variables are critical for the credibility and transparency of the study. In the original version, we acknowledge that the descriptions of Digital Trade (DT) and Green Total Factor Productivity (GTFP) were not sufficiently detailed. In the revised manuscript, we have now added a dedicated subsection in Section 4.3 (Measurement of Key Variables) to clarify the construction of these variables: (1) Green Total Factor Productivity (GTFP): We calculate GTFP using the SMB-ML index based on a directional distance function, consistent with prior studies (Wu et al., 2022). The model incorporates desirable outputs (gross output), inputs (capital, labor, energy), and undesirable outputs (pollutants). As firm-level environmental data in China are indeed limited, the latest data is only up to 2014, We map the pollution data at the city level to each enterprise by industry and scale proportion. We have elaborated on the definition, calculation method and references of GTFP in detail in 4.3.1. (2)Digital Trade Index (DT): At the firm level, direct measures of online sales or digital platform usage are often not publicly available. Following recent literature in this field (Dai et al., 2025), we construct the DT using city-level digital trade development indices, and match them with firm locations (prefecture-level cities) to reflect firms’ digital trade environment exposure. This index captures the regional intensity of digital trade activity, which affects firms’ likelihood and degree of engagement in digital trade. We have provided detailed explanation and data sources in Section 4.3. For your convenience, we have included the revised sections below.

4.3. Variable definition

4.3.1 Explained variable: Corporate GTFP

The GTFP is considered an accurate indicator that takes into account both economic performance and the ecological environment [4]. It is considered an accurate indicator that takes into account both economic performance and the ecological environment. Drawing on the calculation method of Wu et al.[55] the SBM-ML model in data envelopment analysis is used to measure GTFP. Specifically, the model uses capital, labor, and energy consumption as input factors and divides output into expected output, represented by annual enterprise revenue, and unexpected output, represented by enterprise emissions of waste gas (SO₂), wastewater, and dust. The GTFP index can be decomposed into green technology efficiency (GEC) and green technology progress (GTC) through linear programming [55]. GEC stems from efficiency changes brought about by improvements in the production system, economies of scale, and experience accumulation. In contrast, GTC stems from efficiency changes resulting from improvements in production technology and process innovation. The measurement of input and output indicators of enterprise GTFP is shown in Table 1.

Table 1. Construction of GTFP Indicators for Enterprises

Variable		Measurement	Data source
Input	Labor	Number of employees	CSMAR (Referring to Wu et al. (2022))
	Capital	Net fixed assets	CSMAR (Referring to Wu et al. (2022))
	Energy	Urban industrial electricity consumption * number of persons employed in enterprise/number of persons employed in the city	Chinese City Statistical Yearbook (Referring to Wu et al. (2022))
Expected outputs	Business revenue	Annual revenue	CSMAR (Referring to Wu et al. (2022))
Non-expected outputs	Industrial sulfur dioxide (SO₂)	Urban industrial sulfur dioxide * number of persons employed in enterprise/number of persons employed in the city	Chinese City Statistical Yearbook (Referring to Wu et al. (2022))
	Industrial wastewater	Urban industrial wastewater * number of persons employed in enterprise/number of persons employed in the city
	Industrial smoke and dust emissions	Urban industrial smoke and dust emissions* number of persons employed in enterprise/number of persons employed in the city

4.3.2 Explanatory variable: City digital trade

Table 2. Construction of digital trade indicators for cities

First class index	Second class index	Third class index	Unit	Weights	Indicator properties
Infrastructure	Digital network	Number of mobile phone users at the end of the year	unit	0.0333	+
		Internet broadband access port	unit	0.0305	+
		Internet broadband access users	unit	0.0346	+
	Logistics and transportation	Number of employments in transportation, warehousing and postal industries	1 unit	0.0741	+
	Logistics and transportation	Highway mileage	km	0.021	+
Industrial foundation	Information and communication technology (ICT) industry	Employment in Information transmission, software and information technology services in urban units	unit	0.114	+
		Software revenue	unit	0.110
		Telecommunications revenue	Yuan	0.049	+
	E-commerce industry	E-commerce sales	Yuan	0.093	+
		Number of enterprises with e-commerce activities	1 unit	0.050
		Express revenue	Yuan	0.110	+
		Total postal service volume	Yuan	0.087	+
Market potential	Market purchasing power	Per capita consumption expenditure	1 Yuan	0.012	+
	Market purchasing power	Total retail sales of consumer goods	Yuan	0.034	+
	Market internationalization	Actual amount of foreign capital used in the year	US	0.080	+
Digital financial support	Digital Financial	China Digital Financial Inclusion Index	%	0.011	+

4.3.3 Corporate life cycle

Existing research generally agrees that enterprise development goes through a life cycle process[59,60,61]. This study is based on the widely used corporate life cycle measurement method proposed by Anthony and Ramesh [62], with adjustments made to account for the actual differences between industries in China. It draws on the approach adopted by Li et al.(2011)[ 63], which categorizes the business life cycle into growth, maturity, and decline stages based on a composite score of indicators such as revenue growth rate, retained earnings, capital expenditures, and firm age. The reason why this study did not adopt variables such as dividend payments, which appear in life cycle literature, is primarily because Chinese listed companies generally prefer to pay few dividends or no dividends at all, resulting in a weak correlation between dividend payments and company growth. Therefore, this indicator is not suitable for use [63]. In practical implementation, industry differences were considered. The total sample was sorted by industry based on the total score of the four indicators. Each industry sample was then divided into three parts based on total score. The top third with the highest scores were classified as growth-stage companies; the bottom third with the lowest scores were classified as decline-stage companies; and the middle third were classified as mature-stage companies. Finally, the classification results for each industry were aggregated to obtain the classification results for the entire life cycle of all listed companies. The specific classification criteria are shown in Table 3.

Table 3. Criteria for dividing the stages of a company's life cycle

Variable	Revenue growth rate		Retained earnings		Capital expenditures		Firm age
Stage	Feature	Score	Feature	Score	Feature	Score	Feature	Score
Growth	High	3	High	3	High	3	High	3
Maturity	Medium	2	Medium	2	Medium	2	Medium	2
Decline	Low	1	Low	1	Low	1	Low	1

Comment 3:Mechanism Channels – Green Innovation and Supply Chain Coordination: The paper highlights green technological innovation and supply chain coordination as mechanisms, which is a strong point, but the treatment of these mechanisms raises a few concerns. The measurement of “green innovation” should be clarified – presumably, it’s something like the number of green patents or R&D expenditures focused on environmental projects. If it is patents, are these all patents in environment-related classes, and is the data from a particular source (e.g., patent office records)? If it is R&D, how do we distinguish green R&D from general R&D? Similar questions apply to “supply chain coordination” – an interesting but vague construct. The authors might be using a proxy such as whether the firm has an integrated IT system for the supply chain or the number of strategic supplier partnerships. This needs to be explained.

Response 3: We sincerely thank the reviewers for their valuable comments and encouragement. We fully agree that the clear definition of mechanism variables is crucial to the credibility of the analysis. In the original draft, the description of green innovation and supply chain coordination measures is not detailed enough. In the revised original draft (Section 4.3.4 Mechanisms and Threshold Variables), we have added accurate definitions and calculation methods for variables. For your convenience, we have included the revised sections below.

4.3.4 Mechanism and threshold variables

=+−	(4)
	(5)

The threshold variable includes digital trade (DT), environmental regulation (ER), intellectual property protection (IPP), and market integration (MI). In detail, considering that the coordinated development of digital trade and enterprises’ GTFP may be subject to the government and the market: (1)Green environmental regulation (ER) indicators characterizing external environmental constraints, which is obtained by multiplying the share of heavy industry in the GDP of each city in the province with the frequency of ‘environmental protection’ related terms in the work reports of the provincial government; (2)Intellectual property protection(IPP) indicators characterizing the degree of importance attached to scientific and technological innovation, IPP draws on the explicit comparative advantage index proposed by Balassa [66] to construct a city-level IPP index as shown in Eq.(6), represents the intensity of intellectual property protection at the city level, and represent the number of intellectual property trial settlements of the city and China in period t; and represent the GDP of the city and China in year ;(3)Market integration (MI) indicators characterizing enterprises’ production efficiency are introduced as threshold variables, respectively. MI is based on the algorithm of Parsley and Wei [67], which uses the market segmentation index to represent the market integration index. The lower the market segmentation index, the higher the degree of domestic market integration.

(6)

Comment 4:Connection to Finance Literature: The manuscript could do more to tie its findings into the broader finance dialogue. For example, if digital trade indeed improves firms’ green performance, one might expect capital markets to reward such firms (given evidence that investors value sustainability and direct funds towards greener firms. Citing a few key papers in this domain would show awareness – e.g., Hartzmark and Sussman (2019) show that mutual fund flows respond to sustainability ratings, and recent work finds risk disclosure regulations alter investor behavior (Mugerman et al., 2022). Bringing this perspective in the introduction or conclusion would underscore why improving GTFP via digital trade is not just environmentally and operationally beneficial, but potentially financially as well. This would enrich the paper’s appeal to finance-focused readers.

References:

1.Hartzmark, S. M., & Sussman, A. B. (2019). Do investors value sustainability? A natural experiment examining ranking and fund flows. Journal of Finance, 74(6), 2789–2837. https://doi.org/10.1111/jofi.12841

2.Mugerman, Y., Steinberg, N., & Wiener, Z. (2022). The exclamation mark of Cain: Risk salience and mutual fund flows. Journal of Banking & Finance, 134, 106332. https://doi.org/10.1016/j.jbankfin.2021.106332

Response 4: We sincerely thank the reviewer for this very helpful suggestion. We fully agree that linking the findings to the broader finance literature adds important value, and enhances the paper’s appeal to finance-oriented readers. In the revised version, we have now explicitly incorporated this perspective by citing Hartzmark & Sussman (2019) and Mugerman et al. (2022), as recommended by the reviewer. These references are discussed in Section 1 Introduction paragraph 1. These literatures connect digitalization and green production in enterprises very well. On the one hand, the literature provides important evidence that digitalization has accelerated the green information disclosure of enterprises. On the other hand, these literatures link the investor behavior in the micro-market with corporate decisions, emphasizing that investors' green preferences can motivate enterprises to produce in a green way. For your convenience, we have included the revised sections below.

In micro markets, the increased significance of information plays a crucial role in influencing investor behavior[6]. Research shows that, as companies increase their green information disclosure, investors increasingly prioritize sustainability. Companies with high sustainability achieve higher returns in the stock market[7].

Mugerman, Y.; Steinberg, N.; Wiener, Z. The exclamation mark of Cain: Risk salience and mutual fund flows. Journal of Banking & Finance. 2022,134, 106332. https://doi.org/10.1016/j.jbankfin.2021.106332
Hartzmark, S. M.; Sussman, A. B. Do investors value sustainability? A natural experiment examining ranking and fund flows. Journal of Finance, 2019,74(6), 2789–2837. https://doi.org/10.1111/jofi.12841

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have thoroughly addressed and incorporated all comments, resulting in substantial improvements to the manuscript. These revisions have significantly enhanced both the quality and clarity of the article, strengthening its overall contribution to the field. Therefore, I recommend the manuscript for publication.

Comments on the Quality of English Language

The English could be improved to more clearly express the research.

Author Response

Comment 1：The authors have thoroughly addressed and incorporated all comments, resulting in substantial improvements to the manuscript. These revisions have significantly enhanced both the quality and clarity of the article, strengthening its overall contribution to the field. Therefore, I recommend the manuscript for publication. The English could be improved to more clearly express the research.
Response 1：We sincerely appreciate your recognition of the improvements made throughout the manuscript. And thank you very much for your thoughtful suggestion regarding the English expression. In response to your comment, we conducted an additional round of careful language polishing, focusing on clarity, academic tone, and consistency of terminology across the full text. We have also double-checked sentence structures and improved phrasing where necessary to ensure that the research is communicated as clearly and professionally as possible. We are grateful for your recommendation for publication, and we hope the revised version now meets the journal’s standards.

Reviewer 2 Report

Comments and Suggestions for Authors

I can see that you put a lot of time and effort into this revision. I think you did a great job.

Author Response

Comment 1：I can see that you put a lot of time and effort into this revision. I think you did a great job.
Response 1：Thank you very much for your kind and encouraging feedback. We truly appreciate your recognition of the time and effort invested in this revision. Your constructive comments were instrumental in guiding the improvement of the manuscript, and we are grateful for your support throughout the review process. We’re pleased to hear that the revised version meets your expectations.

Reviewer 3 Report

Comments and Suggestions for Authors

Dear authors,
In my opinion, the lack of discussion and broader contextualisation of your research makes the article weak. Most scientists are very good at counting. However, what you bring to the table that is unique and individual are the ideas, plans, solutions, interpretations and recommendations.

Publishing the research alone does not make sense, especially as it limits the possibility of interpretation. The fact that you have added recommendations will broaden your analytical base and potentially increase the number of citations, but the key is the relationship with other issues and research. The lack of such analysis lowers the indexing of your article and makes it difficult to classify, both by the authors themselves and by search engines, including AI indexing.

Not including interpretation limits your ability to disseminate your research results more widely. But of course, that is your choice.

Author Response

Comment 1:In my opinion, the lack of discussion and broader contextualisation of your research makes the article weak. Most scientists are very good at counting. However, what you bring to the table that is unique and individual are the ideas, plans, solutions, interpretations and recommendations.

Publishing the research alone does not make sense, especially as it limits the possibility of interpretation. The fact that you have added recommendations will broaden your analytical base and potentially increase the number of citations, but the key is the relationship with other issues and research. The lack of such analysis lowers the indexing of your article and makes it difficult to classify, both by the authors themselves and by search engines, including AI indexing.

Not including interpretation limits your ability to disseminate your research results more widely. But of course, that is your choice.

Response 1: We sincerely appreciate the reviewer’s constructive and insightful comments, particularly regarding the need for broader contextualization, theoretical interpretation, and meaningful policy engagement. In the revised manuscript, we have made substantial improvements in response to this suggestion: (1)Broader contextualisation: We have strengthened the introduction by situating our research within both global and national policy frameworks, including the Paris Agreement, the United Nations’ 2030 Sustainable Development Goals, and China’s “Dual Carbon” strategy. We also emphasize the timeliness and practical relevance of our work in the discussion section, connecting our findings to pressing digital and green development agendas. (2)Interpretation of results: For each empirical finding, we now provide 1–2 paragraphs explaining its economic and theoretical implications. Drawing on frameworks such as the TOE model and innovation diffusion theory, we clarify the mechanisms behind the observed effects and what they reveal about enterprise behavior and institutional dynamics. (3)Engagement with existing literature: We have added a comparative subsection in the discussion, explicitly contrasting our results with at least 3–5 core studies. We highlight how our study differs in terms of data granularity (city-level vs. provincial), methodological innovation (nonlinear threshold models), and theoretical contribution (firm life cycle heterogeneity and institutional thresholds). (4)Integration of theory and hypotheses: Throughout the empirical section, we explicitly reference the hypotheses and theoretical framework discussed earlier, thereby reinforcing the conceptual–empirical linkages and ensuring analytical coherence. (5)Structured and grounded policy recommendations: The revised policy implications now follow a clear logic—moving from empirical result to policy advice to expected outcome—ensuring that each recommendation is rooted in evidence and relevant to institutional or managerial decision-making. (6)Terminological consistency: We have reviewed and standardized the use of key terms across the manuscript. For example, “green technological innovation” has been revised to “green technology innovation” for alignment with the conceptual definitions used throughout the study. We are grateful for the reviewer’s reminder of the importance of thoughtful interpretation and practical insight. These enhancements aim to ensure that the article contributes not only to academic scholarship but also to the broader discourse on digital trade and sustainable development. For your convenience, we have included the revised sections below.

Keywords: Digital trade; Green total factor productivity; Green technology innovation; Supply chain management; Enterprise life cycle

Introduction

In the context of accelerating global climate change and technological transformation, the pursuit of a green and sustainable economy has become a central policy agenda worldwide. International frameworks such as the Paris Agreement and the United Nations’ 2030 Sustainable Development Goals emphasize the need to achieve low-carbon and innovation-driven growth. In China, national strategies including the “dual carbon” goals, the “Digital China” initiative, and the “14th Five-Year Plan” for green development reflect a strong national commitment to aligning economic modernization with environmental sustainability. To measure firms’ ability to achieve both economic efficiency and environmental responsibility, green total factor productivity (GTFP) has become a widely accepted indicator[1, 2, 3]. Unlike traditional productivity indicators, GTFP considers undesirable outputs, such as carbon and pollutant emissions, and balances economic growth with ecological sustainability[4]. Improving GTFP not only helps firms enhance operational efficiency under resource constraints, but also aligns with rising investor and societal expectations for corporate sustainability. Moreover, rising investor attention to corporate environmental performance underscores the practical value of improving GTFP. In micro markets, the increased significance of information plays a crucial role in influencing investor behavior[5]. Studies show that as firms disclose more green-related information, investors tend to reward companies with higher sustainability ratings through enhanced stock valuations[6]. In this context, increasing GTFP not only helps companies maximize economic efficiency but also aligns with broader societal expectations for sustainable development, thus providing a strategic “win–win” for both enterprises and stakeholders.

Within this broader context, digital trade (DT) has emerged as a transformative force, reshaping how firms operate, compete, and pursue sustainable growth[7,8]. Defined as the provision of goods and services through digital channels and technologies such as big data, cloud computing, and the Internet of Things, digital trade reduces transaction costs, enhances information transparency, and facilitates global market access [9]. In China, supportive policy frameworks and infrastructure investments have accelerated the development of digital trade, providing new avenues for firms to enhance their GTFP [10]. Emerging studies suggest that digital trade may promote GTFP through various channels, such as improving digital financial inclusion, enhancing trade openness, and attracting foreign direct investment [3,11].

5. Results and discussion

5.1. Baseline regression results and discussion

As evidenced in Table 4 baseline regression results, digital trade exerts a statistically significant positive impact on corporate GTFP. After incorporating control variables, the regression coefficient for Digital Trade is 0.004, significant at the 5% level. This indicates that a one-unit increase in urban digital trade development corresponds to an average 0.004-unit improvement in corporate GTFP. This finding aligns with Hypothesis 1 and is consistent with the research results of Dai et al.[11] at the provincial level. The reasearch suggests that digital trade at the city level can enhance firms’ ability to produce more efficiently under environmental constraints, contributing to both economic performance and environmental compliance.

To disentangle transmission channels, we decompose GTFP into technical efficiency change (GEC) and technological progress change (GTC). Columns 3-4 of Table 4 reveal that digital trade predominantly drives GTFP improvement through GEC elevation (β = 0.003, p < 0.05), while its effect on GTC remains statistically insignificant (β = 0.001, p > 0.1). Interestingly, this result contrasts with the findings of Lyu et al. (2022)[13], who report that the positive impact of the digital economy on GTFP is primarily driven by improvements in green technological progress (GTC), rather than technical efficiency (GEC). This divergence can be explained by the fundamental distinction between the digital economy and digital trade. The digital economy encompasses a broad spectrum of digital infrastructure, platforms, and innovation systems, which are often embedded within long-term industrial upgrading policies. In contrast, digital trade focuses more narrowly on the digitization of transactions, supply chains, and commercial interfaces. Consequently, the digital economy may stimulate frontier green innovation through deeper integration of R&D, data-driven manufacturing, and cross-sectoral knowledge spillovers. In contrast, digital trade tends to produce immediate gains in operational coordination, market access, and resource reallocation efficiency, which are more directly reflected in GEC rather than GTC. This distinction highlights the need to differentiate between types of digital transformation when evaluating their environmental and productivity effects.

5.2. Heterogeneity effects across corporate life cycle stages

To examine how firms respond differently to external digital trade environments, we ivestiate the heterogeneous effects of digital trade across corporate life cycle stages. Firms in different phases of the life cycle, such as growth, maturity and decline, face distinct constraints and opportunities regarding resource availability, innovation focus, risk appetite, and strategic flexibility. This perspective aligns with the TOE framework[28], which suggests that technology adoption outcomes depend on external technological and environmental contexts, as well as firm internal environmental readiness. By incorporating the life cycle perspective into our empirical strategy, we aim to reveal how firm maturity influences the extent to which digital trade contributes to GTFP, as well as to identify the channels through which this contribution occurs. This approach addresses intra-city firm heterogeneity that is not captured by city-level indices alone.

As reported in Table 7, the responsiveness of GTFP to digital trade varies significantly across different stages of the corporate life cycle. The results show that GTFP’s responsiveness to digital trade varies significantly depending on a firm’s organizational maturity. Specifically, digital trade has a significantly positive effect on GTFP for growth-stage and declining firms, while its impact on mature firms is statistically insignificant. These results suggest that the effectiveness of digital trade as a lever for green upgrading varies depending on a firm’s internal development stage and strategic orientation.

5.5. Mechanism impact analysis

This section examines the underlying mechanisms through which digital trade enhances firms' GTFP. It focuses on two key channels: green technological innovation and supply chain management.

……

These results yield several meaningful implications. First, they empirically confirm that green innovation and transformation serve as effective channels through which digital trade enhances GTFP. Second, they demonstrate that digital trade’s environmental benefits are contingent on firms’ capacity to integrate digital tools into strategic innovation and process change, rather than merely adopting them passively. Third, they suggest that targeted support for green R&D and transformation—especially in mature-stage firms—can unlock the sustainability potential of digital trade, providing co-benefits for environmental goals and firm performance.

Second, digital trade improves firms’ GTFP by enhancing supply chain transparency and reducing coordination costs, thereby boosting overall supply chain efficiency. Columns 1-4 of Table 10 show that digital trade positively affects supply chain transparency at every stage of the life cycle, though the effect is weaker during the growth phase. Columns 5–8 show that digital trade significantly reduces supply chain coordination costs, especially for firms in the maturity and decline stages. These findings imply that digital trade contributes to both greater supply chain transparency and lower coordination costs, which in turn improve firms’ overall resource efficiency and environmental performance. Within the TOE framework, this reflects the organizational condition that enable firms to reconfigure operational routines in response to digital opportunities. Existing studies have shown that the digitalization of the supply chain can increase GTFP[39]. Therefore, we can reasonably draw Hypothesis 2b that digital trade can optimize supply chain management capabilities and thereby enhance GTFP.

The asymmetry of effects across life cycle stages can be explained by the differing supply chain configurations and complexity. Mature and declining firms typically operate larger, more rigid supply networks, which are more prone to inefficiencies and coordination bottlenecks. These firms also face stronger external pressure to comply with green supply chain regulations and ESG disclosure requirements. Consequently, the adoption of digital trade tools such as blockchain traceability, IoT-based monitoring, and platform-based procurement can generate more substantial efficiency gains in later stages of the corporate life cycle. In contrast, growth-stage firms often maintain more flexible and less fragmented supply chains, which may limit the marginal benefit of digital coordination at early stages. These findings are supported by prior studies that underscore the role of digital technologies in reducing transaction frictions, lowering information asymmetry, and facilitating real-time collaboration among supply chain partners[79, 80].

From a practical perspective, the supply chain management mechanism offers several important implications. Primarily, it emphasises that digital trade should be viewed not only as a tool for external market expansion but also as a means of optimising internal value chains. Also, suggests that supply chain–oriented digital strategies are particularly beneficial for firms in the maturity and decline stages, where coordination costs and operational rigidities are more pronounced. Equally, it implies that policymakers aiming to promote green productivity should prioritize digital infrastructure and standards that enable interoperable, transparent, and low-friction supply networks.

Further non-linear impact explorations

6.1. Non-linear dynamic effects of digital trade

To further explore the non-linear relationship between digital trade and firms’ GTFP, this study imployes a threshold regression model to evaluate the impact of various levels of digital trade development on GTFP outcomes. As shown in Table 11, significant single and double thresholds were identified at 0.0100 and 0.1226, respectively. This indicates that the impact of digital trade on GTFP is not constant, but varies across different stages of digital trade penetration.

The regression estimation in Table 12, column 1, show that the marginal impact of digital trade on gross territorial factor productivity (GTFP) increases non-linearly. At low levels of digital trade (below 0.0100), the effect on GTFP is negative (β=-1.186), suggesting that premature exposure to digital trade may impose adjustment burdens on firms. As digital trade penetration increases (0.010≤DT≤0.1226), the marginal impact becomes strongly positive (β=+0.526). This reflects the phase in which digital technologies effectively integrate with green innovation and resource optimization. Beyond the second threshold, when digital trade exceeds 0.1226, the positive impact remains significant (β=+0.398), though with diminishing marginal returns. This implies a typical U-shaped non-linear effect, where productivity benefits are initially suppressed but subsequently enhanced with digital maturity, before tapering off due to coordination frictions or digital redundancy[81]. In practical terms, these findings suggest that digital trade can significantly enhance green productivity, but only when firms reach a certain level of digital maturity.

These findings suggest that the impact of digital trade on GTFP is non-linear, with diminishing marginal returns at higher penetration levels. This finding has important implications for firms and policymakers. For firms, it emphasises the need to synchronize digital trade strategies with internal capability building, particularly in green innovation and supply chain integration, to avoid early-stage pitfalls or late-stage saturation. For policymakers, the results highlight the importance of phased and targeted digital infrastructure investment, capacity-building support, and coordinated governance to maximize the green productivity returns of digital trade. In particular, attention should be paid to identifying firms or regions that are stuck below the first threshold, where intervention can yield the greatest marginal effect.

6.2. Non-linear dynamic effects of the external environment

To further examine how external institutional factors moderate the relationship between digital trade and firms’ GTFP, this study uses threshold regression models with three key variables: Environmental Regulation (ER), Intellectual Property Protection (IPP), and Market Integration (MI). As shown in Table 11, all three threshold variables exhibit statistically significant double thresholds, 0.0058 and 0.0077 for ER, 0.0607 and 0.0924 for IPP, and 15.3850 and 19.1530 for MI. This result proves the validity of Hypothesis 3a, Hypothesis 3b and Hypothesis 3c, and suggests that institutional quality is not only a background condition but a dynamic determinant of digital trade effectiveness. This finding aligns with the environmental dimension of the TOE framework, highlighting that external institutional conditions, such as regulatory strictness, intellectual property regimes, and market openness, play a critical role in shaping how firms absorb and translate digital trade opportunities into green productivity gains.

Columns 2-4 of Table 12 present the corresponding regression estimates. A pronounced non-linear moderating effect is observed in all cases: favorable institutional conditions significantly enhance the positive impact of digital trade on GTFP compared to the benchmark model in column 1.

When ER is used as the threshold variable, the impact of digital trade on GTFP is always positive. This supports the conclusion of Chen et al.[15] that ER can promote GTFP. However, the threshold analysis in this paper reveals that, as the ER constraint increases, the impact of digital trade on GTFP exhibits a positive non-linear relationship: first increasing, then decreasing. At low levels of regulation (below 0.0058), digital trade has a modest positive effect on GTFP. As regulation strengthens (0.0058≤ER≤0.0077), the positive impact peaks, aligning with the Porter Hypothesis [44], which suggests that well-designed legislation can promote green innovation and productivity. However, when regulation grows more severe (ER>0.0077), the marginal advantage of digital trade declines, most likely due to resource constraints and compliance obligations that hinder enterprises from fully using digital capabilities. These findings show that moderate, market-friendly regulation works together with digital trade to optimize GTFP gains.

When IPP is used as the threshold variable, the connection follows a “N-shaped” non-linear pattern. This is a further exploration based on the research of Mao and Failler[82], who found that the IPP policy can promote the GTFP of Chinese cities. However, we found that under a low IPP levels (<0.0607), digital trade has a positive impact on GTFP. Then, at intermediate IPP levels (0.0607≤IP≤0.0924), the effect becomes negative, possibly due to adjustment costs, reduced knowledge spillovers, or limited technological diffusion under stricter IP regimes [48]. At higher levels of IPP (> 0.0924), the beneficial effect returns, as firms benefit from secure innovation environments and more incentives to invest in green technologies. These findings demonstrate that, while robust IPP is ultimately helpful, transitional phases may present temporary barriers to green innovation spread.

When MI is used as the threshold variable, the impact of digital trade on GTFP is consistently positive. This corroborates Hou and Song [83]’s conclusion that MI can promote provincial GTFP in China. However, this paper’s threshold analysis further reveals that, as the degree of market integration improves, the marginal promoting effect of digital trade on GTFP exhibits a weak non-linear relationship: first increasing, then decreasing. At lower market integration levels (<15.385), digital trade increases GTFP by creating new market channels and facilitating access to green technologies. As market integration increases (15.385≤MI≤19.153), the favorable impact grows due to increased network effects and economies of scale[51]. However, at the upper threshold (> 19.153), marginal gains are marginally reduced, possibly due to market saturation or competitive crowding, which restricts further productivity advances.

Overall, these findings illustrate that digital trade does not operate in an institutional vacuum. The nature and strength of external environmental constraints significantly shape its productivity-enhancing effects. Importantly, the results confirm that moderate environmental regulation, mature IP protection, and optimal market integration jointly form a “supportive institutional window” where the impact of digital trade on GTFP is maximized.

These insights offer key policy implications. Policymakers should avoid both under- and over-regulation, adopting adaptive, staged environmental policies that align digital development with sustainability goals. IP systems should strike a balance between protection and openness to avoid innovation bottlenecks, while market integration efforts must avoid monopolization or over-consolidation. Most critically, institutional reforms should be coordinated with digital trade policy to realize system-wide green productivity gains.

7. Conclusion

This study systematically investigates the nexus between digital trade and corporate GTFP through a life cycle perspective, employing panel data from Chinese A-share listed firms and 287 prefecture-level cities (2012–2022). Empirical results show that: First, digital trade exerts a robust positive impact on GTFP, predominantly mediated by technical efficiency improvements (GEC). Second, the GTFP effects diverge across corporate maturity stages. Enterprises operating within a recession cycle have increased their focus on efficiency-oriented strategies due to technology lock-in and resource constraints. The role of digital trade in promoting GTFP is particularly evident in this context. Third, digital trade enhances GTFP through dual channels: promoting green technological innovation and optimizing supply chain coordination. Forth, our analysis also highlights two layers of non-linear threshold effects: On the one hand, the marginal benefit of digital trade on GTFP shows a U-shaped non-linear dynamic; On the other hand, institutional factors such as environmental regulation, intellectual property protection and market integration further regulate this relationship. According to their levels at different stages, they consistently and dynamically amplify or limit the impact of digital trade on GTFP. These findings underscore the importance of aligning digital trade strategies with supportive and adaptable institutional environments to fully realise their sustainability potential.

These insights yield several managerial and policy implications. First, the non-linear effects of digital trade suggest that regions at different stages of digital development require phased strategies. These strategies should include basic infrastructure and capacity building in lagging areas, as well as platform integration and risk governance in digitally saturated zones. This approach contributes to the prevention of diminishing returns and maximize productivity. Second, green innovation is most responsive to digital trade in mature firms. To support green upgrading, especially in firms with higher absorptive capacity, governments should offer targeted innovation subsidies, patent-sharing mechanisms, or digital R&D tax credits. Third, digital trade improves supply chain efficiency, particularly in the latter stages of the product life cycle. Policies should promote the adoption of digital supply chains, such as IoT-enabled logistics or blockchain traceability, through procurement standards or financial incentives. Fourth, institutional thresholds in areas such as regulation, IP protection, and market integration significantly impact the GTFP effects of digital trade. Policymakers should calibrate these frameworks to avoid over regulation or under protection and ensure that digital transformation aligns with green policy goals. Finally, firm managers should adapt digital-green strategies to match life cycle stages while monitoring regulatory shifts to align transformation efforts with the evolving external environment. This could promote sustainable digital trade–driven green productivity across different contexts.

Compared with existing literature, this study has made progress in both the theoretical and empirical aspects. First, Lyu et al.[13]’s previous research also found that the digital economy has a U-shaped non-linear impact on GTFP. This finding is consistent with our conclusion that digital trade has a U-shaped non-linear impact on GTFP. However, Lyu et al. emphasized the role of green technological progress (GTC) in the digital economy's impact on GTFP. In contrast, our research results show that digital trade mainly enhances GTFP through technological efficiency (GEC), especially at the enterprise level. Second, unlike Dai et al.[11], who adopted provincial digital trade indicators, we constructed a digital trade index at the city level. This index integrates digital inclusive finance and industrial foundation indicators, enabling more accurate identification of regional heterogeneity. Futhermore, this paper analyzes enterprise life cycle heterogeneity, compensating for the inability of regional digital trade indicators to identify the degree to which enterprises in the same region utilize digital trade at different development stages. Finally, this paper introduces environmental factors into the TOE framework through the threshold model. Compared to the studies of Chen et al.[15], Mao and Failler[82], and Hou and Song[83], which examined the linear impact of environmental regulations, intellectual property rights, and market integration on GTFP, this paper further explores the non-linear impact of environmental factors on digital trade and GTFP. The research reveals the significant non-linear relationship between digital trade and GTFP. The research results align with sustainable development theory, technology diffusion theory, and the TOE framework. These results emphasize that promoting GTFP through digital trade requires an institutional background and enterprise maturity. These findings meaningfully contribute to the implementation of global sustainability frameworks, such as the Paris Agreement and the United Nations’ 2030 Sustainable Development Goals, by demonstrating how digitalization can enable low-carbon productivity growth. The study also provides empirical support for China’s dual carbon targets and “Digital China” initiative. The study suggests that coordinated development of digital trade and green transformation is essential for achieving high-quality, sustainable economic development.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Sustainability

Subject: Referee Report for Manuscript ID sustainability-3696225R1

Title: How Does Digital Trade Affect Firm’s Green Total Factor Productivity? A Life Cycle Perspective

The revised paper addresses my earlier points, providing clarifications, particularly regarding the identification strategy and variable measurements.

The theoretical framing and empirical methods are now more explicit, enhancing the analysis. I appreciate the consideration of potential endogeneity through instrumental variables and quasi-experimental approaches, and additional lit.

I suggest addressing some minor points:

Please clarify (very) briefly within the main text how the digital trade index (DTI) accounts explicitly for firm-level heterogeneity. While city-level indices provide a regional context, readers would benefit from an acknowledgment of how potential variations across individual firms within the same city might impact the interpretation of your results.

The discussion around threshold effects (environmental regulations, intellectual property protection, market integration) is clear. Still, an interpretation (in a few words) of the economic or policy implications derived from these thresholds in the conclusion section would provide additional value.

Thank you for your efforts in addressing my comments.

Author Response

Comment 1: Please clarify (very) briefly within the main text how the digital trade index (DTI) accounts explicitly for firm-level heterogeneity. While city-level indices provide a regional context, readers would benefit from an acknowledgment of how potential variations across individual firms within the same city might impact the interpretation of your results.

Response 1: Thank you very much for this valuable suggestion. In response, we revised the manuscript to explicitly acknowledge the potential for firm-level heterogeneity within cities that may not be captured by the city-level Digital Trade Index (DTI). Specifically, we added a new paragraph in Section 5.2 explaining the motivation for incorporating a heterogeneity analysis based on firms’ life cycle stages. We examined corporate life cycle stages because firms at different stages (growth, maturity, and decline) have significant differences in strategic priorities, innovation capacities, and responsiveness to external digital trade environments. This approach is theoretically grounded in the TOE framework, which emphasizes the importance of internal environmental readiness in technology adoption. Thus, the life cycle perspective enables us to better understand how firm-level differences influence the impact of city-level digital trade development on green productivity outcomes. We believe this addition clarifies our empirical strategy and directly addresses the reviewer’s concern. For your convenience, we have included the revised sections below.

5.2. Heterogeneity effects across corporate life cycle stages

Comment 2: The discussion around threshold effects (environmental regulations, intellectual property protection, market integration) is clear. Still, an interpretation (in a few words) of the economic or policy implications derived from these thresholds in the conclusion section would provide additional value.

Response 2: We sincerely thank the reviewer for highlighting this important point. In response, we have substantially revised the conclusion section to explicitly interpret the economic and policy implications of the identified threshold effects. Firstly, we now emphasize that the nonlinear nature of environmental regulation, intellectual property protection, and market integration either amplifies or constrains the impact of digital trade on green technology frontier production (GTFP). We no longer treat these institutional factors as static controls, but rather analyze them as dynamic conditions that can enhance or inhibit the green productivity benefits of digital trade, depending on their levels. Secondly, based on the threshold analysis and other empirical results, five policy suggestions were specifically given. We hope this enhanced interpretation provides the additional value requested by the reviewer and meaningfully strengthens the practical relevance of our findings. For your convenience, we have included the revised sections below.

7. Conclusion

Author Response File: Author Response.pdf

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How Does Digital Trade Affect a Firm’s Green Total Factor Productivity? A Life Cycle Perspective

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