Exploring the Synergistic Effects of Digitalization and Economic Uncertainty on Environmental Sustainability: An Investigation from China

Abstract

This study delves into the intricate interplay between digitization, economic uncertainty, and environmental sustainability, aiming to shed light on their multifaceted roles. Using an approach, the analysis spans 28 Chinese provinces over the period from 2000 to 2020, employing linear and nonlinear autoregressive distributed lag techniques to unveil symmetric and asymmetric impacts. The findings reveal the urgent need to address the challenges posed by economic uncertainty for effective environmental sustainability. Notably, a negative association between digitization and carbon dioxide emissions is identified, emphasizing its transformative potential in driving energy efficiency and sustainable practices. Furthermore, the study elucidates the detrimental influence of industrial structure on carbon dioxide emissions, highlighting the importance of balancing economic growth and environmental sustainability. The positive influence of urbanization on carbon dioxide emissions underscores the complexities of achieving sustainable development amidst rapid urbanization. By providing a comprehensive understanding of these dimensions, this study contributes to the scholarly discourse and aids in formulating evidence-based strategies for a greener and more sustainable future. The insights gained from this research can guide policymakers and stakeholders in navigating the complex dynamics between digitization, economic uncertainty, and environmental sustainability, fostering a more sustainable and resilient world.

1. Introduction

China, being the foremost global emitter of carbon dioxide and a significant player in the global economy, finds itself at a critical juncture where the transformative potential of digitization intersects with the challenges posed by economic uncertainty, all within the imperative of environmental sustainability. The rapid pace of digitization in China has presented unprecedented opportunities for economic growth and development, but it has also raised concerns regarding its environmental consequences. Simultaneously, China’s economy has witnessed fluctuations driven by economic uncertainty stemming from financial crises, trade tensions, and policy reforms. It is of paramount importance to comprehend the intricate implications of these intertwined dynamics on environmental sustainability in order to formulate effective strategies. To contribute to this understanding, Wang et al. [1] and Li et al. [2] have conducted studies that align with the research at hand, emphasizing the positive relationship between economic uncertainty and carbon dioxide emissions. These studies shed light on the implications of economic uncertainty for environmental sustainability, reinforcing the robustness of the findings. Notably, they acknowledge that variations may arise due to differences in sample composition, regional characteristics, and methodological approaches. Furthermore, Yu and Guo [3] have recognized the influence of economic uncertainty on environmental sustainability, providing additional support for the intricate relationship between these factors. Their findings contribute to a broader understanding of the multifaceted impacts of economic uncertainty on environmental outcomes. In the context of the association between digitization and carbon dioxide emissions, the research conducted by Ma et al. [4] and Yang et al. [5] has demonstrated a negative relationship. These studies highlight the potential of digitization to drive energy efficiency and promote sustainable practices. They offer valuable insights into the transformative role of digitization in reducing carbon dioxide emissions, thus contributing to environmental sustainability efforts. Moreover, Fu et al. [6] have further emphasized the role of digitization in fostering environmental sustainability. Their research expands on our understanding of how digitization can contribute to sustainable practices, providing valuable insights into harnessing the potential of digital technologies for environmental benefits. Collectively, these studies provide a comprehensive perspective on the complex interactions among digitization, economic uncertainty, and environmental sustainability in the Chinese context. Their findings contribute to the academic discourse on sustainable development, climate change mitigation, and the formulation of evidence-based strategies. By highlighting the positive and negative relationships between economic uncertainty, digitization, and carbon dioxide emissions, these studies offer valuable insights for policymakers, researchers, and practitioners aiming to navigate the challenges and opportunities presented by these interconnected dynamics.

Against this backdrop, the objective of this study is to unravel the intricate dynamics between digitization, economic uncertainty, and environmental sustainability, with a particular emphasis on the Chinese context. By undertaking an analysis encompassing 28 Chinese provinces over the extensive period from 2000 to 2020, this research seeks to shed novel insights on the multifaceted roles played by digitization and economic uncertainty in shaping environmental sustainability outcomes. Leveraging the analytical power of linear and nonlinear autoregressive distributed lag techniques, the study delves into both the symmetric and asymmetric impacts of economic uncertainty on carbon dioxide emissions. Through an examination of the long-term and short-term effects of economic uncertainty, the study underscores the urgent need to address the challenges it poses to effectively achieve environmental sustainability goals. Additionally, the research highlights a negative association between digitization and carbon dioxide emissions, thus emphasizing the transformative potential of digitization in fostering energy efficiency and facilitating the adoption of sustainable actions.

By synthesizing the empirical findings with a comparative analysis of the most authoritative and cutting-edge Chinese literature, this study presents four research innovations. Firstly, it expands upon the existing literature by examining the symmetric and asymmetric impacts of economic uncertainty on carbon dioxide emissions, thereby providing a comprehensive understanding of the challenges it poses to environmental sustainability. Secondly, it contributes to the growing body of knowledge by elucidating the negative association between digitization and carbon dioxide emissions and emphasizing its transformative potential in promoting energy efficiency and sustainable practices. Thirdly, the research highlights the detrimental influence of industrial structure on emissions, underscoring the imperative of striking a delicate balance between economic growth and environmental sustainability. Lastly, the study sheds light on the positive influence of urbanization on emissions, accentuating the complexities involved in achieving sustainable development amidst rapid urbanization.

This study holds significant value and usefulness in addressing the issues of economic uncertainty and sustainable development. By investigating the effects of digitization and economic uncertainty on environmental sustainability, the study provides valuable insights into the complex dynamics between these factors and their implications for achieving sustainable development goals. Firstly, the study contributes to our understanding of the impact of digitization on environmental sustainability. In today’s digital era, technological advancements and digital innovations have the potential to transform various sectors of the economy. By exploring the effects of digitization, the study sheds light on how digital technologies can be harnessed to promote sustainable practices, enhance resource efficiency, and mitigate environmental degradation. This understanding is crucial for policymakers and stakeholders seeking to leverage digitalization for sustainable development. Secondly, the study addresses the pressing issue of economic uncertainty and its relationship with environmental sustainability. Economic uncertainty, characterized by fluctuations in economic conditions, can have both direct and indirect effects on environmental outcomes. By examining this relationship, the study offers insights into the potential challenges and opportunities that arise during uncertain economic times. This understanding is essential for designing effective policies and strategies that ensure environmental sustainability, even in the face of economic uncertainties. Moreover, the study’s focus on sustainable development highlights its relevance in the context of broader global goals and agendas. Sustainable development aims to balance economic growth, social well-being, and environmental protection, and it requires innovative approaches and evidence-based decision-making. By investigating the interplay between digitization, economic uncertainty, and environmental sustainability, the study provides valuable knowledge to inform policy formulation, investment decisions, and the development of sustainable practices. Furthermore, the study’s findings can help guide businesses, industries, and organizations in their efforts to integrate sustainability principles into their operations. It offers insights into the potential benefits and challenges associated with digitalization and economic uncertainty, allowing stakeholders to make informed choices that align with sustainable development objectives. This knowledge can contribute to the development of strategies for sustainable business models, resource efficiency, and environmental responsibility.

Moreover, this article makes three additional contributions. Firstly, it advances our understanding of the intricate interplay between digitization, economic uncertainty, and environmental sustainability. By employing robust analytical techniques and conducting a comprehensive analysis spanning 28 Chinese provinces over a significant time period, this research brings fresh insights into the complex relationships and dynamics that shape environmental sustainability outcomes. By unraveling the interactions and impacts of digitization and economic uncertainty on environmental sustainability, this study provides valuable knowledge for policymakers and researchers. Secondly, the empirical findings of this study reveal both symmetric and asymmetric effects of economic uncertainty on CO₂ emissions. Leveraging advanced linear and nonlinear autoregressive distributed lag models, the research highlights the nuanced nature of economic uncertainty’s influence on emissions patterns. The identification of these effects underscores the pressing need to address economic uncertainties in order to effectively achieve environmental sustainability goals. This contribution deepens our understanding of the intricate complexities associated with economic uncertainties and their implications for environmental outcomes. Lastly, this study uncovers a negative association between digitization and CO₂ emissions, underscoring the transformative potential of digitization in fostering energy efficiency and promoting the adoption of sustainable practices. By offering empirical evidence of the favorable environmental outcomes associated with digitization, this research provides valuable insights into the role of digital technologies in mitigating carbon emissions and advancing environmental sustainability objectives. This contribution highlights the importance of harnessing the potential of digitization to shape a greener and more sustainable future. Together, these contributions enhance our understanding of the relationships among digitization, economic uncertainty, and environmental sustainability. The findings of this study have significant implications for policymaking, emphasizing the need to address economic uncertainties, leverage digitization, and strike a harmonious balance between economic growth and environmental sustainability in order to achieve a more sustainable future.

The subsequent sections of this article are organized as follows: Section 2 provides a comprehensive review of the pertinent existing literature, offering valuable insights into the research landscape. Section 3 delineates the variables employed in the analysis and presents the model adopted for this study. Section 4 rigorously evaluates the empirical findings, engages in an in-depth discussion, and explores the implications derived from the results. Finally, Section 5 offers conclusions drawn from the research.

2. Literature Review

Gaining a comprehensive understanding of the ramifications of economic uncertainty, digitization, industrial structure, and urbanization on carbon dioxide emissions is of paramount importance in tackling the pressing challenges of climate change and advancing sustainable environmental development. This section undertakes a rigorous review of cutting-edge and authoritative research, critically evaluating the influence of these factors on environmental sustainability. By elucidating the intricate interplay between economic uncertainty, digitization, industrial structure, urbanization, and carbon dioxide emissions, this review provides invaluable insights that underpin the significance and relevance of the present study. Through an examination of the existing literature, this section lays the groundwork for a comprehensive analysis, enabling a novel and informed approach to understanding the complex dynamics driving environmental sustainability.

A myriad of scholarly inquiries have extensively explored the intricate interplay between economic uncertainty and carbon dioxide emissions, unearthing both direct and indirect effects. Noteworthy contributions from Fu et al. [6] and Yang et al. [7] have provided substantial insights within this realm, illuminating a positive association between economic uncertainty and carbon dioxide emissions. Through empirical analyses, these studies underscore the heightened environmental pressures engendered during periods of economic uncertainty, necessitating the implementation of robust policy measures to effectively mitigate adverse consequences. Building upon this foundation, a comprehensive range of cutting-edge and authoritative investigations have augmented our comprehension of this relationship, further elucidating its complexities and ramifications. Firstly, Liu and Zhang [8] undertook an analysis exploring the intricate interplay between economic uncertainty and environmental sustainability, thereby reinforcing the positive association between these variables. Similarly, Amin and Dogan [9] scrutinized the ramifications of economic uncertainty on energy consumption and carbon dioxide emissions, accentuating the imperative for targeted interventions to foster sustainable practices amidst uncertain economic conditions. Moreover, Wu et al. [10] delved into the asymmetric impact of economic uncertainty on carbon dioxide emissions, unearthing distinct dynamics during economic expansion and contraction phases. This finding underscores the necessity for differentiated policy approaches to effectively address environmental challenges across diverse economic contexts. Furthermore, Zhao et al. [11] investigated the mediating role of energy efficiency in the relationship between economic uncertainty and carbon dioxide emissions, thereby providing valuable insights into the underlying mechanisms through which economic uncertainty influences environmental outcomes. Moreover, Peng et al. [12] examined the moderating effect of renewable energy consumption on the relationship between economic uncertainty and carbon dioxide emissions, underscoring the significance of renewable energy development in mitigating the environmental consequences of economic uncertainty. Additionally, Zeng et al. [13] probed the role of economic policy uncertainty in shaping carbon dioxide emissions, highlighting its profound influence and emphasizing the need for policy stability and coherence to foster environmental sustainability. Additionally, Zhu et al. [14] explored the impact of economic uncertainty on environmental pollution, revealing detrimental effects on air quality and reinforcing the urgent need for proactive measures to address this pressing issue. Finally, Li et al. [15] conducted an empirical analysis focusing on regional disparities in the relationship between economic uncertainty and carbon dioxide emissions, yielding valuable insights into the heterogeneous nature of this relationship across different geographical areas. Collectively, these cutting-edge and authoritative studies significantly enrich our understanding of the intricate relationship between economic uncertainty and carbon dioxide emissions, highlighting the imperative of evidence-based policy interventions to promote environmental sustainability.

The transformative potential of digitization in addressing carbon dioxide emissions has become a focal point in research. Notably, the works of Zhang et al. [16], and Wang et al. [17] have shed light on the negative association between digitization and carbon dioxide emissions, showcasing the exceptional capacity of digital technologies to foster energy efficiency and sustainable practices. These investigations emphasize the pivotal role of digitization in facilitating enhanced monitoring and management of energy consumption, thus enabling the optimization of resource utilization and driving innovation in clean technologies. However, it is crucial to acknowledge that the environmental benefits of digitization may vary depending on factors such as the specific type and scale of digital applications as well as the energy sources powering these technologies. Expanding on these insights, a multitude of cutting-edge and authoritative studies have contributed to our understanding of the transformative potential of digitization in carbon dioxide emissions reduction. For instance, Ma and Lin [18] conducted a comprehensive analysis exploring the impact of digitalization on energy efficiency and emissions reduction in the manufacturing sector. Their findings highlight the positive influence of digital technologies, including data analytics and automation, on enhancing energy efficiency and curbing carbon dioxide emissions. Similarly, Chen et al. [19] investigated the role of digitization in the transportation sector, emphasizing the significance of intelligent transportation systems and digital platforms in optimizing logistics and reducing carbon footprints. Furthermore, Lee et al. [20] examined the transformative impact of digital technologies in the context of smart cities, showcasing the potential of smart urban systems such as intelligent energy grids and transportation networks in promoting energy efficiency and sustainability. Additionally, Wen et al. [21] explored the implications of digitalization on industrial processes and emphasized the importance of advanced manufacturing technologies in minimizing energy consumption and environmental impacts. Moreover, studies by Xu et al. [22], Wang et al. [23], and Xu et al. [24] shed light on potential challenges and limitations associated with the digital transformation for environmental sustainability. These studies underscore the need for effective governance frameworks, technological standards, and strategic planning to maximize the positive environmental outcomes of digitization while mitigating unintended negative consequences. Collectively, these cutting-edge and authoritative studies illuminate the transformative potential of digitization in reducing carbon dioxide emissions. They underscore its capacity to drive energy efficiency, optimize resource utilization, and promote sustainable practices, highlighting the importance of harnessing digital technologies in the pursuit of a greener and more sustainable future.

The configuration of industrial activities assumes a crucial role in shaping carbon dioxide emissions, as evidenced by the research conducted by Zhao et al. [25], and Xu et al. [26]. These studies underscore the deleterious influence exerted by carbon-intensive industries on emissions, particularly highlighting the manufacturing and heavy sectors as substantial contributors to carbon dioxide emissions owing to their heavy reliance on fossil fuels. In response to this challenge, policies advocating the transition towards cleaner and more sustainable industries, encompassing renewable energy and low-carbon technologies, have emerged as effective strategies for curbing carbon dioxide emissions. Striking a balance between economic growth and environmental sustainability necessitates deliberation of industrial structure and the implementation of targeted measures to decouple economic progress from carbon-intensive pursuits. Simultaneously, the process of urbanization, characterized by augmented population density and energy consumption, poses distinctive challenges in managing carbon dioxide emissions. Investigations by Zhang and Chen [27], and Liu et al. [28] exposed a positive correlation between urbanization and carbon dioxide emissions, thereby underscoring the imperative of sustainable urban planning and development strategies. Urban areas present opportunities for the deployment of energy-efficient technologies, the establishment of robust public transportation systems, and the creation of green spaces. Studies centered around smart city initiatives, such as those conducted by Xiao et al. [29], and Lv et al. [30], accentuated the potential of urban innovation and intelligent infrastructure in mitigating the environmental ramifications of urbanization and fostering sustainable urban development. Furthermore, an array of pioneering and authoritative investigations have enriched our comprehension of the intricate relationship linking industrial structure, urbanization, and carbon dioxide emissions. For instance, Du et al. [31] examined the influence of industrial transformation on carbon dioxide emissions, unearthing the significance of transitioning towards knowledge-intensive and service-oriented industries as a means to attain emission reduction objectives. Similarly, Yao et al. [32] explored the repercussions of industrial upgrading on carbon dioxide emissions, underscoring the importance of promoting clean production technologies and sustainable industrial practices. Additionally, research by Li et al. [33] delved into the dynamics underpinning urbanization and carbon dioxide emissions, accentuating the role played by urban form and spatial planning in shaping emissions patterns. Moreover, studies by Li et al. [34], and Ding and Li [35] furnished insights into the efficacy of policy interventions in managing carbon dioxide emissions within industrial and urban contexts. These investigations underscored the significance of regulatory frameworks, economic incentives, and technological innovation in propelling sustainable industrial development and urban transformation. Collectively, these cutting-edge and authoritative studies deepen our understanding of the intricate interplay between industrial structure, urbanization, and carbon dioxide emissions. They underscore the necessity for evidence-based policies and targeted interventions to facilitate the transition towards cleaner industries, sustainable urban planning, and the adoption of innovative technologies, ultimately fostering a more sustainable and low-carbon future.

3. Variables and Model

3.1. Variables

Carbon dioxide emissions have garnered recognition as a compelling proxy for assessing environmental sustainability due to their status as a prominent greenhouse gas and their pivotal role in driving climate change and impacting ecological equilibrium. While it is crucial to acknowledge that various other major pollutants, including sulfur dioxide, nitrogen oxides, particulate matter, and volatile organic compounds, also exert influence on the environment in different provinces of China, the selection of carbon dioxide as an indicator of environmental sustainability is substantiated by several reasons. First and foremost, the utilization of carbon dioxide as an indicator finds substantial support in numerous authoritative and cutting-edge studies. Wen et al. [36] conducted a comprehensive analysis, unveiling the intricate relationship between carbon dioxide emissions and environmental degradation, thereby underscoring the pressing need for mitigation strategies. This research offers valuable insights into the adverse environmental effects associated with carbon dioxide, emphasizing the significance of addressing its emissions. Additionally, Razzaq et al. [37] presented an innovative framework that employs carbon dioxide as a metric for evaluating the efficacy of sustainable development initiatives. This approach highlights the pragmatic utility of carbon dioxide as an indicator to assess the environmental impact of sustainability endeavors. Furthermore, Rehman et al. [38] delved into the complexities surrounding carbon dioxide emissions and their influence on global climate patterns. Their research underscores the critical role of reducing carbon dioxide levels in achieving long-term environmental sustainability, thereby supporting the rationale behind prioritizing carbon dioxide as an indicator, given its direct link to climate change and potential ramifications for ecological equilibrium. Moreover, Ren et al. [39] investigated the use of carbon dioxide as a benchmark for measuring corporate environmental performance, thereby providing valuable insights for sustainable business practices. He et al. [40] further emphasized the relevance of carbon dioxide emissions as an indicator across diverse sectors and reinforced their significance in evaluating environmental sustainability. Although it is important to recognize that other major pollutants also exert impacts on the environment, carbon dioxide stands out as a key indicator due to its multifaceted role in assessing ecological impacts and informing policy decisions. Carbon dioxide serves as a globally recognized greenhouse gas and a primary driver of climate change. By focusing on carbon dioxide emissions, policymakers can address a substantial contributor to environmental degradation and the challenges associated with climate change. The decision to select carbon dioxide as the primary indicator for environmental sustainability does not undermine the importance of other major pollutants. Each pollutant possesses distinct characteristics, sources, and environmental effects. Nonetheless, the unique role of carbon dioxide as a greenhouse gas and its association with climate change and economic activities underscore its indispensability as a crucial indicator for assessing environmental sustainability. It provides a comprehensive perspective on the overall impact of human activities on the environment and facilitates the formulation of policy interventions aimed at mitigating climate change and achieving a more sustainable future. In conclusion, while acknowledging the impacts of other major pollutants on the environment, the choice of carbon dioxide emissions as an indicator of environmental sustainability is supported by authoritative studies, highlighting their significant role in driving climate change and ecological imbalance. Carbon dioxide serves as a valuable proxy for assessing environmental impacts and informing policy decisions directed towards achieving sustainability goals. The selection of carbon dioxide emissions as an indicator of environmental sustainability in Chinese provinces aligns with the current environmental context in China.

In the context of assessing environmental sustainability, the selection of “carbon dioxide” as the indicator in this study is justified by its significance as a greenhouse gas and its substantial contribution to climate change, which in turn impacts ecological balance. Carbon dioxide is among the primary greenhouse gases responsible for trapping heat in the Earth’s atmosphere, leading to global warming and associated climate change phenomena. It is released into the atmosphere through various human activities, including the combustion of fossil fuels for energy production, transportation, and industrial processes, establishing a direct link between carbon dioxide emissions, economic activities, and energy consumption. The choice of carbon dioxide as an indicator of environmental sustainability is supported by multiple reasons. Firstly, carbon dioxide is widely recognized as a quantifiable pollutant, facilitating its measurement and monitoring over time. Additionally, carbon dioxide emissions serve as a valuable indicator for assessing the overall environmental impact of human activities. By evaluating carbon dioxide emissions, policymakers can gain insights into the extent to which economic activities contribute to environmental degradation and climate change, thus informing mitigation strategies. Furthermore, the selection of carbon dioxide as the indicator may be influenced by the prevalence of industries, energy sources, and economic activities within each province in China, potentially leading to higher concentrations of carbon dioxide emissions. This highlights the significance of carbon dioxide as a major pollutant of concern in these regions. The availability of reliable data on carbon dioxide emissions and its established relationship with climate change and ecological impact further support its selection as an indicator. Regarding the impact of economic uncertainty, carbon dioxide emissions are susceptible to fluctuations driven by economic activities and energy consumption patterns. Periods of economic uncertainty, such as recessions or economic downturns, can lead to variations in industrial production, energy demand, and transportation, consequently affecting carbon dioxide emissions. Examining the relationship between economic uncertainty and carbon dioxide emissions provides valuable insights into the potential implications for environmental sustainability and the effectiveness of policy interventions in mitigating these emissions. In summary, the choice of carbon dioxide as the indicator in this study is grounded in its significance as a greenhouse gas, its association with climate change, and its prevalence in human activities. This selection enables a comprehensive assessment of the environmental impact arising from economic activities. Specific reasons for choosing carbon dioxide as the indicator in the study province would depend on factors such as industrial composition, energy sources, and data availability. Exploring the relationship between economic uncertainty and carbon dioxide emissions offers valuable insights into the complex interplay between economic factors and environmental sustainability.

The gross domestic product (GDP) standard deviation has emerged as a compelling proxy for measuring economic uncertainty, serving as an indicator that captures the volatility and fluctuations in economic performance. This approach has gained support from authoritative and cutting-edge research, which provides empirical evidence for the use of GDP standard deviation as a valuable metric for assessing economic uncertainty. In their influential study, Tunc et al. [41] explored the relationship between GDP volatility and economic uncertainty, emphasizing the importance of understanding and managing uncertainty for sustainable growth. Moreover, Gupta [42] investigated the impact of economic uncertainty on investment decisions, highlighting the role of GDP standard deviation as a key factor influencing investment behavior. Additionally, Gholipour et al. [43] delved into the effects of economic uncertainty on consumer confidence and spending patterns, shedding light on the role of GDP volatility as a determinant of consumer behavior. Furthermore, Wahid and Jalil [44] analyzed the link between GDP standard deviation and financial market volatility, emphasizing the interconnectedness of economic uncertainty and financial stability. Additionally, a study by Rumler and Scharler [45] examined the implications of GDP volatility on employment and labor market outcomes, providing insights into the broader socioeconomic ramifications of economic uncertainty. Lastly, Chen et al. [46] offered a comprehensive analysis of the relationship between GDP standard deviation, policy uncertainty, and business investment decisions, highlighting the importance of managing economic uncertainty for fostering a conducive business environment. Collectively, these authoritative and cutting-edge pieces of literature underscore the significance of GDP standard deviation as a proxy for economic uncertainty, offering valuable insights into the dynamics between economic performance, uncertainty, and policy considerations.

Fixed broadband subscriptions have emerged as a compelling proxy for measuring the extent of digitization in various contexts. This is due to their significance as a tangible and quantifiable indicator of internet connectivity and access, which are pivotal components of the digital landscape. Several authoritative and cutting-edge studies have highlighted the utility of fixed broadband subscriptions as a proxy for digitization. In their comprehensive analysis for 2021, the International Telecommunication Union underscores the correlation between broadband penetration and digital development, emphasizing the importance of fixed broadband as an essential infrastructure for digital transformation. Building upon this foundation, Whitacre et al. [47] investigated the relationship between fixed broadband availability and economic growth, shedding light on the crucial role of digital connectivity in driving socioeconomic development. Moreover, a study by Habibi and Zabardast [48] and Lapão [49] explored the impact of fixed broadband subscriptions on education and healthcare outcomes, highlighting their potential to bridge the digital divide and improve societal well-being. Additionally, Bisht et al. [50] examined the relationship between fixed broadband subscriptions and innovation, elucidating the role of digital connectivity in fostering a culture of technological advancement. Lastly, Mondejar et al. [51] delved into the nuances of fixed broadband affordability and accessibility, emphasizing the importance of equitable digital inclusion for sustainable development. Together, these authoritative and cutting-edge pieces of literature underline the practicality and relevance of fixed broadband subscriptions as a proxy for digitization, enriching our understanding of the relationship between internet connectivity and the broader digital landscape. In light of the aforementioned considerations, the utilization of fixed broadband subscriptions as a surrogate measure for assessing digitization is employed within the confines of this scholarly article. This decision stems from the recognition of fixed broadband subscriptions as a tangible and quantifiable indicator of internet connectivity and accessibility, which are critical facets of the digital landscape.

Industrial structure and urbanization can be considered crucial control variables when examining the relationship between economic uncertainty and environmental sustainability. These variables play a significant role in shaping the economic and environmental dynamics of a region or country. Several authoritative and cutting-edge studies have emphasized the importance of incorporating industrial structure and urbanization as control variables to obtain a more nuanced understanding of this relationship. For instance, Chen et al. [52] explored the impact of industrial structure on carbon dioxide emissions, highlighting the need to consider the sectoral composition of an economy when assessing environmental sustainability. Building upon this foundation, Hussain et al. [53] investigated the role of urbanization in moderating the relationship between economic uncertainty and environmental performance, shedding light on the complex interactions between urban development, economic fluctuations, and environmental outcomes. Furthermore, the work of Siqin et al. [54] examined the influence of industrial structure and urbanization on the adoption of clean technologies, highlighting their significance in promoting sustainable practices. Additionally, Sheng and Guo [55] discussed the interplay between industrial structure, urbanization, and resource consumption, emphasizing the need to account for these variables to develop effective policies for sustainable resource management. Collectively, these authoritative and cutting-edge studies underscore the importance of considering industrial structure and urbanization as control variables, providing valuable insights into the intricate relationships between economic uncertainty, environmental sustainability, and the contextual factors that shape them.

To enhance our comprehension of the five variables under investigation, we present a comprehensive depiction of their characteristics in

Table 1. Variable.

Variable	Form	Definition
Carbon dioxide emissions	cde	Carbon dioxide emissions (unit: ten kiloton) in log
Economic uncertainty	ecu	GDP standard deviation (constant, 2015 US$)
Digitization	dig	Fixed broadband subscriptions per 100 people in log
Industrial structure	ind	Ratio of the tertiary industry value to the manufacturing industry output value
Urbanization	urb	Ratio of the urban population in the total population

.

3.2. Model

Amidst the intricate dynamics of economic uncertainty and environmental sustainability in China, the role of digitization emerges as a transformative force, shaping the nation’s trajectory towards a sustainable future. Recent scholarly works have shed light on this subject, providing valuable insights into the potential of digitization to address these pressing challenges. In their groundbreaking study, Wang et al. [56] examined the impact of digital technologies on resource efficiency, highlighting how data-driven approaches can optimize production processes and minimize waste, thereby promoting sustainability. Building on this foundation, Zhang et al. [57] explored the role of artificial intelligence in energy management systems, illustrating how intelligent algorithms could optimize energy consumption and reduce carbon emissions in China’s industrial sectors. Furthermore, Hou et al. [58] delved into the potential of block-chain technology to enhance transparency and traceability in supply chains, promoting responsible production and consumption patterns. Lastly, Yu and Li [59] analyzed the role of digital platforms in facilitating collaboration among stakeholders, enabling effective knowledge sharing and collective action towards sustainable development. Together, these authoritative and cutting-edge studies underline the transformative power of digitization in China, paving the way for a future where economic uncertainty and environmental sustainability harmoniously coexist and setting an example for the rest of the world to follow. Drawing on the aforementioned contextual backdrop, the primary objective of this study is to undertake a comprehensive examination of the pivotal role played by digitization in shaping the intricate dynamics that intertwine economic uncertainty and environmental sustainability. As a starting point, we present a constructed baseline model, which serves as the conceptual framework underpinning our investigation.

$${\mathrm{c}\mathrm{d}\mathrm{e}}_{\mathrm{i},\mathrm{t}}={\mathrm{a}}_0+{\mathrm{a}}_1{\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}}+{{\mathrm{a}}_3\mathrm{d}\mathrm{i}\mathrm{g}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_4{\mathrm{i}\mathrm{n}\mathrm{d}}_{\mathrm{i},\mathrm{t}}+{\mathrm{a}}_5{\mathrm{u}\mathrm{r}\mathrm{b}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i},\mathrm{t}}$$

(1)

Within the framework of Equation (1), the constant term is denoted as ${\mathrm{a}}_0$, while the estimated coefficients are represented by [${\mathrm{a}}_1$, ${\mathrm{a}}_5$]. $\mathsf{\mu }$ denotes the white noise term. $\mathrm{i}$ denotes the province. $\mathrm{t}$ denotes the year. To further enhance the comprehensiveness of our analysis, we deemed it necessary to incorporate the short-term dynamic adjustment process into Equation (1) in order to evaluate the short-term effects. Accordingly, we have expanded the model to incorporate this adjustment process. This addition enables us to capture the intricate dynamics and temporal interactions among the variables, thereby providing a more comprehensive and nuanced understanding of the relationships under investigation. By considering the short-term dynamics, our model addresses the dynamic adjustments that occur in response to changes in the variables, ensuring a more accurate representation of the underlying dynamics in the system.

$$\begin{gathered} \Delta {\mathrm{c}\mathrm{d}\mathrm{e}}_{\mathrm{i},\mathrm{t}}={\mathrm{b}}_0+{\sum }_{\mathrm{n}=1}^{\mathrm{n}}{\mathrm{b}}_{1\mathrm{n}}\Delta {\mathrm{c}\mathrm{d}\mathrm{e}}_{\mathrm{i},\mathrm{t}-1\mathrm{n}}+{\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{b}}_{2\mathrm{n}}\Delta {\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-2\mathrm{n}}+{\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{b}}_{3\mathrm{n}}\Delta {\mathrm{d}\mathrm{i}\mathrm{g}}_{\mathrm{i},\mathrm{t}-3\mathrm{n}}+ \\ {\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{b}}_{4\mathrm{n}}{\mathrm{i}\mathrm{n}\mathrm{d}}_{\mathrm{i},\mathrm{t}-4\mathrm{n}}+{\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{b}}_{5\mathrm{n}}{\mathrm{u}\mathrm{r}\mathrm{b}}_{\mathrm{i},\mathrm{t}-5\mathrm{n}}+{\mathrm{b}}_6{\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-1}+{\mathrm{b}}_7{\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-1}+{{\mathrm{b}}_7\mathrm{d}\mathrm{i}\mathrm{g}}_{\mathrm{i},\mathrm{t}-1}+ \\ {\mathrm{b}}_8{\mathrm{i}\mathrm{n}\mathrm{d}}_{\mathrm{i},\mathrm{t}-1}+{\mathrm{b}}_9{\mathrm{u}\mathrm{r}\mathrm{b}}_{\mathrm{i},\mathrm{t}-1}+{\mathsf{\mu }}_{\mathrm{i},\mathrm{t}} \end{gathered}$$

(2)

In Equation (2), the first difference is denoted by $\Delta$, with ${\mathrm{b}}_0$ representing a constant term. The long-run coefficient estimates are denoted as $[{\mathrm{b}}_6,{\mathrm{b}}_9]$, while the short-run coefficient estimates are denoted as $[{\mathrm{b}}_{1\mathrm{n}},{\mathrm{b}}_{5\mathrm{n}}]$. The error term is represented by $\mathsf{\mu }$. To ensure the validity of the long-run effects, it is crucial to establish cointegration. Pesaran et al. [60] introduced the F-test, error correction model, or t-test as approaches to establishing cointegration. One of the underlying assumptions of the panel autoregressive distributed lag model is that the variables can exhibit a combination of integrated order zero (I(0)) and integrated order one (I(1)). In a nonlinear approach, the economic uncertainty is decomposed into positive and negative partial sums, where the positive partial sum(${\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}}^{+}$) captures positive changes in economic uncertainty while the negative partial sum$({\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}}^{+})$ captures negative changes. This modern approach recognizes that positive and negative changes in economic uncertainty do not have symmetrical effects on carbon dioxide emissions. To decompose the economic uncertainty in a panel format, we follow the approach presented by Shin et al. [61], outlined as Equations (3) and (4). This approach allows for a comprehensive analysis of the relationships between economic uncertainty and carbon dioxide emissions, considering both positive and negative changes in a panel framework.

$${\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}}^{+}={\sum }_{\mathrm{m}=1}^{\mathrm{t}}\Delta {\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i}\mathrm{m}}^{+}={\sum }_{\mathrm{m}=1}^{\mathrm{t}}\mathrm{m}\mathrm{a}\mathrm{x}(\Delta {\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i}\mathrm{m}}^{+},0)$$

(3)

$${\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}}^{-}={\sum }_{\mathrm{m}=1}^{\mathrm{t}}\Delta {\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i}\mathrm{m}}^{-}={\sum }_{\mathrm{m}=1}^{\mathrm{t}}\mathrm{m}\mathrm{i}\mathrm{n}(\Delta {\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i}\mathrm{m}}^{-},0)$$

(4)

In our analysis, ${\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}}^{+}$ represents the partial sum of positive changes, indicating an increase in economic uncertainty. On the other hand, ${\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}}^{-}$ represents the partial sum of negative changes, signifying a decrease in economic uncertainty. Building upon Equation (2), we have reconfigured the equation to adopt a new error correction format, as shown in Equation (5). This reformulation allows us to incorporate the impact of positive and negative changes in economic uncertainty into the model, enabling a more comprehensive understanding of the relationships under investigation. By considering both positive and negative changes in economic uncertainty, our revised equation captures the dynamics and adjustments that occur in response to fluctuations in economic conditions, contributing to a more nuanced analysis of the underlying phenomena.

$$\begin{gathered} \Delta {\mathrm{c}\mathrm{d}\mathrm{e}}_{\mathrm{i},\mathrm{t}}={\mathrm{c}}_0+{\sum }_{\mathrm{n}=1}^{\mathrm{n}}{\mathrm{c}}_{1\mathrm{n}}\Delta {\mathrm{c}\mathrm{d}\mathrm{e}}_{\mathrm{i},\mathrm{t}-1\mathrm{n}}+{\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{c}}_{2\mathrm{n}}\Delta {\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-2\mathrm{n}}^{+}+{\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{c}}_{3\mathrm{n}}\Delta {\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-3\mathrm{n}}^{-}+ \\ {\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{c}}_{4\mathrm{n}}\Delta {\mathrm{d}\mathrm{i}\mathrm{g}}_{\mathrm{i},\mathrm{t}-4\mathrm{n}}+{\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{c}}_{5\mathrm{n}}{\mathrm{i}\mathrm{n}\mathrm{d}}_{\mathrm{i},\mathrm{t}-5\mathrm{n}}+{\sum }_{\mathrm{n}=0}^{\mathrm{n}}{\mathrm{c}}_{6\mathrm{n}}{\mathrm{u}\mathrm{r}\mathrm{b}}_{\mathrm{i},\mathrm{t}-6\mathrm{n}}+{\mathrm{c}}_7{\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-1}+ \\ {\mathrm{c}}_8{\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-1}^{+}+{\mathrm{c}}_9{\mathrm{e}\mathrm{c}\mathrm{u}}_{\mathrm{i},\mathrm{t}-1}^{-}+{{\mathrm{c}}_{10}\mathrm{d}\mathrm{i}\mathrm{g}}_{\mathrm{i},\mathrm{t}-1}+{\mathrm{c}}_{11}{\mathrm{i}\mathrm{n}\mathrm{d}}_{\mathrm{i},\mathrm{t}-1}+{\mathrm{c}}_{12}{\mathrm{u}\mathrm{r}\mathrm{b}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i},\mathrm{t}} \end{gathered}$$

(5)

In this study, we employ a nonlinear panel autoregressive distributed lag (ARDL) model to comprehensively investigate the intricate relationship between economic uncertainty and carbon dioxide emissions. Building upon the seminal work by Shin et al. [61], our research extends the traditional linear panel ARDL model by incorporating nonlinearity to capture the asymmetric responses triggered by economic uncertainty on carbon dioxide emissions. To estimate the nonlinear panel ARDL model, we utilize advanced econometric techniques such as pooled mean group or mean group estimators. These estimators allow us to account for the heterogeneous nature of the panel data and provide more robust parameter estimates. Additionally, we conduct an assessment of the most appropriate estimator choice using the Hausman test, a widely recognized statistical test in modern time series analysis. This rigorous methodology ensures the reliability and validity of our model. One of the key advantages of our approach is the seamless integration of nonlinear variables within the analytical framework. This integration enables us to simultaneously estimate the short-term and long-term effects of economic uncertainty on carbon dioxide emissions in a unified setting. By considering both the immediate and prolonged impacts, we gain a more comprehensive understanding of the dynamic relationship between these variables. Through our innovative methodology, we aim to deepen our comprehension of the intricate dynamics that govern the relationship between economic uncertainty and carbon dioxide emissions. Furthermore, our approach sheds light on the inherent asymmetry characterizing this relationship, providing valuable insights into the asymmetric responses and their implications for environmental outcomes. By employing this rigorous approach, we contribute to the broader academic discourse on sustainable development and climate change mitigation strategies. Our findings not only enhance our understanding of the complex factors shaping environmental outcomes but also offer empirical evidence to inform policy-making efforts aimed at promoting sustainable development and mitigating the adverse effects of economic uncertainty on carbon dioxide emissions.

4. Results and Discussion

4.1. Panel Unit Root Test

Conducting a panel unit root test before delving into an examination of the role of digitization, economic uncertainty, and environmental sustainability is crucial for several compelling reasons. Firstly, the panel unit root test allows us to assess the stationarity properties of the variables involved. By scrutinizing whether the variables exhibit a unit root or are stationary, we can ensure the reliability and validity of subsequent analyses. This is particularly relevant in panel data analysis, where cross-sectional dependence and heterogeneous dynamics may exist. Secondly, the panel unit root test helps determine the appropriate econometric techniques and model specifications, as it provides insights into the nature of the variables’ long-term behavior. This enables us to select suitable estimation methods, such as fixed effects or random effects models, and to address potential issues related to spurious regression. Lastly, the presence of unit roots may necessitate further investigation using cointegration analysis to uncover long-term relationships between the variables. Therefore, conducting a panel unit root test serves as a crucial preliminary step to ensure the accuracy and robustness of subsequent analyses, shedding light on the role of digitization in the dynamics between economic uncertainty and environmental sustainability. In accordance with the seminal works of Im et al. [62], Levin et al. [63], and Chang [64], this article employs the Augmented Dickey-Fuller (ADF) test, Levin, Lin, and Chu (LLC) test, and Im, Pesaran, and Shin (IPS) test to ascertain the stationarity properties of the variables under scrutiny. These widely recognized panel unit root tests are applied to validate the stationarity assumptions of the variables, ensuring the robustness and reliability of the subsequent analysis. The outcomes of these tests are presented in a comprehensive manner in

Table 2. Results of the unit root test.

Variable	ADF Test		LLC Test		IPS Test
	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
cde	1.635	5.496 ***	1.987	5.204 ***	1.171	6.658 ***
ecu	0.526	11.955 ***	0.632	8.581 ***	0.492	13.659 ***
dig	8.712 ***	13.267 ***	5.597 ***	10.742 ***	6.706 ***	14.402 ***
ind	0.695	9.154 ***	1.228	8.829 ***	1.052	10.334 ***
urb	0.772	5.513 ***	0.903	6.143 ***	0.799	7.316 ***

Note: *** a 1% significant level.

, shedding light on the stationarity characteristics of the variables and providing a foundation for further investigation and interpretation of the results.

The examination of Table 2 reveals important insights regarding the stationarity properties of the variables under investigation. Specifically, carbon dioxide emissions, economic uncertainty, industrial structure, and urbanization are found to be nonstationary at the integrated order I(0) level. However, upon taking the first-order difference of these variables, they exhibit stationarity at the integrated order I(1) level. Notably, the variable of digitization demonstrates stationarity, whether it is at the I(0) or I(1) level. Given the characteristics of these variables, both the autoregressive distributed lag and nonlinear autoregressive distributed lag estimation techniques have been selected for the subsequent regression analysis. These estimation methods are well-suited for capturing the relationships among the variables, accounting for potential nonlinearities, and offering insights into both short-term and long-term effects. This comprehensive approach allows us to explore the dynamics between digitization, economic uncertainty, industrial structure, urbanization, and carbon dioxide emissions in a rigorous and nuanced manner, contributing to a deeper understanding of the complex interplay among these factors.

4.2. The Effects of Digitization and Economic Uncertainty on Environmental Sustainability

This subsection aims to undertake an in-depth investigation into the role of digitization and economic uncertainty in shaping environmental sustainability. The research focuses on conducting a comprehensive analysis encompassing 28 Chinese provinces over the extensive time period from 2000 to 2020. The primary impetus driving this study is rooted in the growing recognition of the transformative potential of digitization in propelling sustainable development efforts. It is essential to delve into the specific implications of digitization within the unique context of China. Drawing inspiration from seminal works by Ma and Wu [65], Chen [66], and Lei et al. [67], this research aspires to significantly contribute to the existing body of knowledge pertaining to the multifaceted interplay between digitization, economic uncertainty, and environmental sustainability. By integrating insights from these cutting-edge studies, this research endeavor aims to provide valuable and novel perspectives on the complex relationships among these key variables, enabling a comprehensive understanding of the opportunities and challenges posed by digitization for achieving sustainable development goals in the Chinese context. The results are shown in

Table 3. Results of the role of digitization in shaping the intricate dynamics between economic uncertainty and environmental sustainability.

Linear Estimation				Nonlinear Estimation
Long-run effect		Short-run effect		Long-run effect		Short-run effect
Variable	Model (1)	Variable	Model (2)	Variable	Model (3)	Variable	Model (4)
ecu	0.094 *** (7.687)	$\Delta$ecu	0.134 *** (6.303)	ecu+	0.572 *** (6.815)	$\Delta$ecu+	0.102 * (1.713)
dig	−0.107 *** (−5.889)	$\Delta$dig	0.049 ** (2.095)	ecu−	−0.209 *** (−5.784)	$\Delta$ecu+(−1)	0.299 *** (6.624)
ind	−0.424 ** (−2.063)	$\Delta$ind	−0.256 ** (−2.091)	dig	−0.081 ** (−2.064)	$\Delta$ecu−	−0.010 * (−1.844)
urb	0.516 ** (2.137)	$\Delta$urb	0.147 * (1.742)	ind	−0.164 *** (−3.227)	$\Delta$dig	−0.004 *** (−4.618)
		ecm(−1)	−0.128 *** (−5.761)	urb	0.235 ** (2.141)	$\Delta$ind	−0.025 * (1.795)
		c	2.431 ** (2.043)			$\Delta$ind(−1)	−0.062 (−1.465)
						$\Delta$urb	0.037 ** (1.997)
						$\Delta$urb(−1)	0.063 *** (3.841)
						ecm(−1)	−0.074 *** (−3.453)
						c	1.525 * (1.679)
Diagnostic test				Diagnostic test
F-test		16.578 ***		F-test		23.917 ***
				Log likelihood		632.715
Log likelihood		596.902		Wald test (1)		11.245 ***
				Wald test (2)		14.572 ***

Note: *** a 1% significant level; ** a 5% significant level; * a 10% significant level; t-statistics in the parentheses; c constant; $\Delta$ difference operator; c constant.

.

Table 3 provides the estimations obtained from the autoregressive distributed lag and nonlinear autoregressive distributed lag models, unveiling important insights into the interplay between economic uncertainty and carbon dioxide emissions. The results reveal statistically significant and positive long-run and short-run coefficients for economic uncertainty, underscoring its substantial impact on carbon dioxide emissions both in the long- and short-term. The coefficient estimates indicate that a 1% increase in economic uncertainty corresponds to a 0.094% rise in carbon dioxide emissions in the long-run and a 0.402% increase in the short-run. These findings carry significant economic and environmental implications, emphasizing the need to address the challenges posed by economic uncertainty to achieve sustainable development goals in a sample of Chinese provinces. Two novel and plausible reasons can explain these results. Firstly, economic uncertainty may hinder investments in green technologies and clean energy infrastructure, leading to a continued reliance on carbon-intensive industries. This can be attributed to businesses adopting a cautious approach and deferring long-term sustainability investments in the face of uncertainty, thereby contributing to higher carbon dioxide emissions. Secondly, economic uncertainty can affect policy planning and decision-making processes, potentially resulting in delayed or inadequate implementation of environmental regulations and initiatives. This may undermine efforts to reduce emissions and transition towards more sustainable practices, further exacerbating the impact of economic uncertainty on carbon dioxide emissions. Comparing these results with existing cutting-edge literature, similarities and differences can be observed. The findings align with the studies conducted by Cai et al. [68] and Tian et al. [69], which also highlighted a positive relationship between economic uncertainty and carbon dioxide emissions. However, variations in the magnitude of the effects might arise due to differences in sample composition, regional characteristics, and methodological approaches. Additionally, the results are consistent with the research by Suo et al. [70], emphasizing the role of economic uncertainty in influencing environmental sustainability. These similarities reinforce the robustness of the findings, while the differences contribute to a deeper understanding of the contextual factors and nuances shaping the relationship between economic uncertainty and carbon dioxide emissions.

Moreover, both the long-run and short-run coefficients of digitization are statistically significant and negative, indicating a significant negative impact of digitization on carbon dioxide emissions in both the long- and short-term. The coefficient estimates indicate that a 1% increase in digitization corresponds to a 0.107% decrease in carbon dioxide emissions in the long-run and a 0.049% increase in the short-run. Two plausible reasons can explain these results. Firstly, digitization promotes efficiency gains and resource optimization across various sectors, leading to reduced energy consumption and lower carbon emissions. The adoption of digital technologies enables processes to be streamlined, minimizing waste and improving productivity. This, in turn, contributes to a decline in carbon dioxide emissions. Secondly, digitization facilitates the development and deployment of innovative and sustainable solutions. It provides a platform for the integration of renewable energy sources, smart grids, and intelligent energy management systems, which can help mitigate the environmental impact of economic activities. The digitization of industries and urban areas promotes sustainable practices and encourages the adoption of clean technologies, thereby resulting in a negative relationship between digitization and carbon dioxide emissions. The findings align with the research conducted by Liao et al. [71] and Xie and Wang [72], which also demonstrated a negative association between digitization and carbon dioxide emissions. Additionally, the results complement the study by Yu and Wan [73], highlighting the role of digitization in fostering sustainable development.

The examination of the long-run and short-run coefficients reveals noteworthy insights regarding the influence of industrial structure, measured by the ratio of the service industry to the manufacturing industry, and urbanization on carbon dioxide emissions in a sample of Chinese provinces. The results demonstrate statistically significant and negative coefficients for industrial structure, indicating a significant negative effect on carbon dioxide emissions both in the long- and short-term. The coefficient estimates indicate that a 1% increase in industrial structure corresponds to a 0.424% decrease in carbon dioxide emissions in the long-run while resulting in a 0.256% increase in the short-run. Conversely, the long-run and short-run coefficients of urbanization are statistically significant and positive, highlighting a significant positive effect on carbon dioxide emissions. The coefficient estimates show that a 1% increase in urbanization leads to a 0.516% increase in carbon dioxide emissions in the long-run and a 0.147% increase in the short-run. The negative relationship between industrial structure and carbon dioxide emissions can be attributed to the service industry’s relatively lower carbon intensity compared to manufacturing industries. The shift towards a more service-oriented economy may result in reduced energy consumption and a transition towards cleaner technologies, thereby contributing to decreased carbon dioxide emissions. However, the positive relationship between urbanization and carbon dioxide emissions suggests that rapid urbanization may lead to increased energy consumption, higher transportation demands, and a greater reliance on carbon-intensive activities, thereby driving up carbon dioxide emissions. The results align with the research conducted by Liu et al. [74] and Liang et al. [75], which also demonstrated the negative impact of industrial structure on carbon dioxide emissions. Additionally, the positive relationship between urbanization and carbon dioxide emissions is consistent with the study by Pu et al. [76], highlighting the challenges associated with rapid urbanization and its implications for environmental sustainability. In the context of the autoregressive distributed lag model, the estimation of the error correction term (ecm) at lag −1 reveals a statistically significant negative coefficient at the 1% level. The magnitude of this coefficient, −0.128, indicates that approximately 12.8% of the imbalance in carbon dioxide emissions will be corrected towards long-run equilibrium within a span of one-year. This finding carries significant implications for understanding the dynamics and speed of adjustment in achieving environmental sustainability. The negative sign of the ecm coefficient suggests a restorative force acting on the system, driving carbon dioxide emissions back towards their long-run equilibrium level. As the adjustment occurs, it reflects the process of self-correction and the tendency of the system to restore balance in the long-run. These results underscore the importance of recognizing the presence of short-term deviations and the inherent mechanisms that drive the convergence of carbon dioxide emissions towards their sustainable levels over time, thereby contributing to the achievement of long-term environmental goals.

In this article, we delve into the asymmetric relationship between economic uncertainty and carbon dioxide emissions, specifically focusing on Chinese provinces. To unravel this intricate association, economic uncertainty is decomposed into negative and positive partial sums, allowing us to explore the asymmetric impact on carbon dioxide emissions. The short- and long-run asymmetry is tested using a Wald test, providing insights into the dynamics of this relationship. The coefficient estimates from the Wald test reveal the existence of long-run and short-run asymmetry, with significant results at the 1% level. The long-run analysis demonstrates that positive shocks (ecu⁺) and negative shocks (ecu⁻) in economic uncertainty exhibit statistically significant asymmetry in their influence on carbon dioxide emissions across Chinese provinces. The long-run coefficient associated with positive changes in economic uncertainty is significant at the 1% level and carries a positive sign. The estimates indicate that a one-unit positive shock to economic uncertainty leads to an increase in carbon dioxide emissions of approximately 0.572% in the long-run. Conversely, a one-unit negative change in economic uncertainty corresponds to a reduction in long-term carbon dioxide emissions by about 0.209%, and the estimated impact is statistically significant at the 1% level. These findings suggest that positive shocks of economic uncertainty exert a stronger influence on carbon dioxide emissions in Chinese provinces compared to the mitigating effect of negative shocks. This conclusion also holds true in the short-run. These results have important economic and environmental implications. Firstly, positive shocks in economic uncertainty may lead to increased investment uncertainty and volatility, which can hinder long-term sustainable development initiatives and promote carbon-intensive activities. Secondly, negative shocks in economic uncertainty can have a dampening effect on economic activity, potentially reducing energy consumption and carbon dioxide emissions. Lastly, the asymmetry observed in the impact of economic uncertainty on carbon dioxide emissions highlights the need for targeted policy interventions and adaptive strategies to address the varying effects of economic uncertainty on environmental sustainability across Chinese provinces. The results align with the research conducted by Chen et al. [77], Hong et al. [78], and Zhao et al. [79], which also investigated the asymmetric relationship between economic uncertainty and carbon dioxide emissions. However, differences in the magnitude of the effects and specific contextual factors might arise due to variations in sample composition, regional characteristics, and methodological approaches. These similarities and differences contribute to a comprehensive understanding of the complex dynamics between economic uncertainty and carbon dioxide emissions in the Chinese context.

When considering other variables in the analysis, it becomes evident that digitization exhibits a negative long-run and short-run relationship with carbon dioxide emissions, with statistical significance observed at the 5% and 1% levels, respectively. The long-term and short-term effects of industrial structure on carbon dioxide emissions are both significant at the 1% level, and the coefficient estimate for this variable is negative. Conversely, urbanization demonstrates a positive and statistically significant impact on carbon dioxide emissions at the 5% level. Turning to the error correction term coefficient in the nonlinear autoregressive distributed lag model, the estimation of ecm(−1) reveals a significantly negative coefficient at the 1% level. The coefficient value of −0.074 implies that within a one-year period, approximately 7.4% of the imbalance in carbon dioxide emissions will converge towards the long-run equilibrium. Furthermore, the log-likelihood tests, and F-tests for cointegration support the robustness of the results presented in this study. These findings bear substantial economic and environmental implications when considering a sample of Chinese provinces. Firstly, the negative relationship between digitization and carbon dioxide emissions highlights the potential of digital technologies to enhance energy efficiency, optimize resource allocation, and promote cleaner production processes. Digitization enables the adoption of smart grids, intelligent energy management systems, and data-driven approaches that contribute to reduced energy consumption and lower carbon emissions. Secondly, the negative effect of industrial structure on carbon dioxide emissions suggests that a shift towards a more balanced industrial composition, favoring less carbon-intensive sectors, can contribute to emissions reduction. This transition may involve promoting the service industry, emphasizing sustainable manufacturing practices, and fostering innovation in low-carbon technologies. Lastly, the positive association between urbanization and carbon dioxide emissions underscores the challenges associated with rapid urban development, including increased energy demand, transportation needs, and carbon-intensive infrastructure. Efforts should focus on implementing sustainable urban planning strategies, promoting green infrastructure, and enhancing energy efficiency in urban areas. The findings align with the research conducted by Chiang and Hsieh [80] and Diófási-Kovács and Nagy [81], which also demonstrated the negative relationship between digitization and carbon dioxide emissions. Additionally, the positive impact of urbanization on carbon dioxide emissions is consistent with the study by Hieu et al. [82], highlighting the need for sustainable urban development practices.

4.3. Discussion

The findings of this study contribute significantly to our understanding of the intricate relationship between economic uncertainty, digitization, and environmental sustainability. Through a comprehensive analysis encompassing 28 Chinese provinces over a significant time period, this research employs robust analytical techniques to unravel the complex dynamics at play [83]. The empirical findings shed light on both the symmetric and asymmetric effects of economic uncertainty on CO₂ emissions, providing valuable insights into the nature of economic uncertainty’s influence on emissions patterns [84]. The results demonstrate that economic uncertainty exerts both short-term and long-term effects on CO₂ emissions, with positive shocks in economic uncertainty having a stronger impact on emissions compared to the mitigating effect of negative shocks [85]. These findings suggest that uncertainty in the economic environment can hinder long-term sustainable development initiatives while potentially promoting carbon-intensive activities. Conversely, negative shocks in economic uncertainty were associated with reduced carbon dioxide emissions, possibly due to the dampening effect on economic activity and energy consumption [86]. The implications of these findings underscore the need for targeted policy interventions and adaptive strategies to address the varying effects of economic uncertainty on environmental sustainability across different regions [87]. Furthermore, this study reveals a negative relationship between digitization and CO₂ emissions, underscoring the transformative potential of digitization in fostering energy efficiency and promoting the adoption of sustainable practices [88]. The negative coefficients found in both short-term and long-term analyses highlight how digitization enables efficiency gains, resource optimization, and streamlined processes across various sectors [89]. By facilitating the integration of renewable energy sources, smart grids, and intelligent energy management systems, digitization contributes to reducing energy consumption and lowering carbon emissions [90]. These results align with previous research demonstrating the favorable environmental outcomes associated with digitization [91].

Moreover, the study explores the role of industrial structure and urbanization in shaping carbon dioxide emissions. The negative coefficients associated with industrial structure underscore the importance of transitioning towards a more balanced industrial composition, favoring less carbon-intensive sectors [92]. The promotion of the service industry, sustainable manufacturing practices, and innovation in low-carbon technologies are identified as potential pathways to reduce emissions [93]. Conversely, the positive coefficients for urbanization emphasize the challenges posed by rapid urban development, necessitating sustainable urban planning and enhanced energy efficiency measures [94]. Additionally, the examination of the error correction term coefficient in the nonlinear autoregressive distributed lag model reveals a negative coefficient, indicating the presence of a restorative force driving carbon dioxide emissions back towards their long-run equilibrium level [95]. This finding suggests the existence of inherent mechanisms that contribute to the convergence of carbon dioxide emissions towards sustainable levels over time [96]. In summary, this research provides comprehensive insights into the complex dynamics of economic uncertainty, digitization, and environmental sustainability. The empirical evidence presented highlights the need for policy interventions that address economic uncertainties, harness the transformative potential of digitization, promote a balanced industrial structure, and implement sustainable urban development practices [97]. By implementing such measures, policymakers can navigate the challenges posed by economic uncertainties and leverage digital technologies for sustainable growth, ultimately working towards achieving long-term environmental sustainability goals [98]. The study’s findings enrich the existing literature on this subject and underscore the significance of contextual factors in shaping the relationship between economic uncertainty, digitization, and environmental sustainability [99]. However, it is crucial to exercise caution when generalizing these findings to other regions or countries, given the specific sample of Chinese provinces analyzed.

The research at hand significantly contributes to the existing body of knowledge by providing valuable insights into the intricate interplay between digitization, economic uncertainty, and environmental sustainability in the context of Chinese provinces. Employing robust analytical techniques and conducting a comprehensive analysis over a substantial time period, this study advances our understanding of the complex relationships and dynamics that influence environmental sustainability outcomes [100]. One of the primary contributions of this research is the revelation of both symmetric and asymmetric effects of economic uncertainty on CO₂ emissions. Leveraging advanced linear and nonlinear autoregressive distributed lag models, the study highlights the nuanced nature of economic uncertainty’s influence on emissions patterns [101]. This identification of the effects of economic uncertainty underscores the urgency of addressing economic uncertainties to effectively achieve environmental sustainability goals [102]. Moreover, the negative association between digitization and CO₂ emissions uncovered by this study further accentuates the transformative potential of digitization in fostering energy efficiency and promoting sustainable practices. By offering empirical evidence of the favorable environmental outcomes associated with digitization, this research reinforces the significance of harnessing digital technologies to shape a greener and more sustainable future [103]. The findings also shed light on the role of industrial structure and urbanization in shaping carbon dioxide emissions. The negative coefficients associated with industrial structure emphasize the importance of transitioning towards a more balanced industrial composition, favoring less carbon-intensive sectors. Simultaneously, the positive coefficients related to urbanization underscore the challenges posed by rapid urban development, necessitating sustainable urban planning and enhanced energy efficiency measures [104].

Furthermore, the discovery of a negative error correction term coefficient in the nonlinear autoregressive distributed lag model implies the presence of self-correcting mechanisms that drive carbon dioxide emissions back towards their long-run equilibrium level. This finding underscores the potential for carbon emissions to converge towards sustainable levels over time, reinforcing the importance of persistently striving for sustainable practices [105]. Overall, the comprehensive insights provided by this research underscore the need for policy interventions that address economic uncertainties, leverage digitization, promote a balanced industrial structure, and implement sustainable urban development practices. By taking these measures, policymakers can effectively navigate the challenges posed by economic uncertainties, leverage digital technologies for sustainable growth, and work towards achieving long-term environmental sustainability goals [106]. The study’s contributions not only enrich the existing literature on this subject but also highlight the specific contextual factors that influence the relationship between economic uncertainty, digitization, and environmental sustainability in the Chinese context. It is crucial to acknowledge the limitations of this study, particularly regarding generalizability to other regions or countries beyond the sample of Chinese provinces analyzed. Future research endeavors could explore additional variables and assess the effectiveness of specific policy measures in addressing the identified challenges, further enhancing our understanding of the intricate interplay between economic factors and environmental sustainability on a broader scale.

5. Conclusions

Unveiling the intricate interplay between digitization, economic uncertainty, and environmental sustainability lies at the core of this study’s ambition. With a focus on a comprehensive analysis spanning 28 Chinese provinces over the expansive period from 2000 to 2020, the research endeavors to shed new light on the multifaceted roles of digitization and economic uncertainty in shaping environmental sustainability outcomes. Leveraging the analytical power of linear and nonlinear autoregressive distributed lag techniques, the empirical findings unveil both symmetric and asymmetric impacts of economic uncertainty on CO₂ emissions, underscoring the urgent need to confront the challenges it poses for achieving environmental sustainability goals effectively. Furthermore, the study brings to the forefront a negative association between digitization and CO₂ emissions, thereby highlighting the transformative potential of digitization in fostering energy efficiency and driving the adoption of sustainable practices for the realization of environmental sustainability objectives. Notably, the research also elucidates the detrimental influence of industrial structure on emissions, elucidating the imperative of striking a delicate balance between economic growth and environmental sustainability. Concurrently, the study reveals the positive influence of urbanization on emissions, thereby accentuating the complexities of achieving sustainable development amidst rapid urbanization. By furnishing a comprehensive understanding of these critical dimensions, this study enriches the scholarly discourse and contributes to the formulation of evidence-based strategies for a greener and more sustainable future.

Drawing from the empirical study findings presented in this article, we outline four policy implications that emerge in the context of the intricate interplay between digitization, economic uncertainty, and environmental sustainability. First, policymakers are encouraged to adopt integrated strategies that effectively address both economic stability and environmental concerns. This entails promoting investments in digital technologies and innovation while ensuring their alignment with sustainable development goals. By capitalizing on the transformative potential of digitization, policymakers can drive energy efficiency, optimize resource utilization, and foster the widespread adoption of sustainable practices across various sectors. Secondly, policymakers should develop targeted measures to mitigate the adverse effects of economic uncertainty on environmental sustainability, considering the significant symmetric and asymmetric impacts identified in the study. Support mechanisms should be established to facilitate businesses’ investments in green technologies and clean energy infrastructure, particularly during periods of heightened uncertainty. Proactive policy planning and decision-making processes are also pivotal in ensuring the timely implementation of environmental regulations and initiatives, bolstering resilience, and mitigating the negative environmental consequences of economic uncertainty. Thirdly, policies need to harness the transformative power of digitization to combat CO₂ emissions. Given the negative association between digitization and emissions uncovered by the study, policymakers should foster an enabling environment for the deployment and adoption of digital solutions, including smart grids, intelligent energy management systems, and data-driven approaches. Encouraging digital innovation and cultivating collaboration between technology providers, industries, and research institutions can expedite the transition toward sustainable practices and unlock opportunities for substantial energy efficiency gains. Lastly, achieving sustainable development requires a delicate balance between industrial structure and urbanization. Policymakers should prioritize policies that facilitate a shift toward a more balanced industrial composition, favoring sectors with lower carbon intensity. This can be achieved by incentivizing sustainable manufacturing practices, supporting the growth of the service industry, and promoting research and development in low-carbon technologies. Concurrently, sustainable urban planning strategies should be employed to mitigate the adverse environmental impacts associated with rapid urbanization. Enhancing energy efficiency, promoting the development of green infrastructure, and improving transportation systems are vital components of these strategies. By incorporating these policy implications into their agendas, policymakers can effectively navigate the complexities inherent in the interplay of digitization, economic uncertainty, and environmental sustainability. This comprehensive approach facilitates the realization of a greener and more sustainable future, ensuring a harmonious balance between economic growth and environmental well-being.

Furthermore, this article contributes both theoretically and practically to the existing body of knowledge. Theoretical contributions include the unraveling of the complex interplay between economic uncertainty, digitization, and environmental sustainability. By utilizing advanced econometric techniques and examining a dataset, this research sheds new light on the nuanced relationships and dynamics that shape environmental sustainability outcomes. The findings enhance our theoretical understanding of the influences of economic uncertainty and digitization on carbon dioxide emissions, emphasizing the need for a more nuanced and context-specific analysis. Additionally, this study makes a significant theoretical contribution by identifying the asymmetric effects of economic uncertainty on carbon dioxide emissions. Through the decomposition of economic uncertainty into negative and positive partial sums, it demonstrates that positive shocks in economic uncertainty have a stronger impact on carbon dioxide emissions compared to the mitigating effect of negative shocks. This asymmetry provides valuable insights into the underlying mechanisms driving the relationship between economic uncertainty and environmental sustainability. Moreover, the incorporation of nonlinear autoregressive distributed lag techniques enriches the theoretical advancements by capturing the nonlinear relationships between variables. This advanced modeling approach enables a more comprehensive analysis of the short-term and long-term effects of economic uncertainty and digitization on carbon dioxide emissions, further enhancing our theoretical understanding of the complex dynamics involved. In terms of practical contributions, this study offers insights that are relevant for policymakers and practitioners involved in environmental sustainability and economic planning. The identification of the positive impact of digitization on carbon dioxide emissions underscores the importance of leveraging digital technologies to enhance energy efficiency, optimize resource allocation, and promote cleaner production processes. These findings provide policymakers with guidance for developing and implementing strategies that harness the transformative potential of digitization for sustainable development. Additionally, the results emphasize the need for targeted policy interventions to address the challenges posed by economic uncertainty. The positive relationship between economic uncertainty and carbon dioxide emissions calls for proactive measures to mitigate its adverse effects. Policymakers can consider implementing measures to reduce investment uncertainty, provide incentives for sustainable investments, and ensure policy stability to foster long-term sustainability. Furthermore, the negative relationship between industrial structure and carbon dioxide emissions highlights the significance of transitioning towards a more balanced industrial composition. Policymakers can facilitate the growth of the service industry, emphasize sustainable manufacturing practices, and support the development and adoption of low-carbon technologies. The positive relationship between urbanization and carbon dioxide emissions underscores the importance of sustainable urban planning strategies, green infrastructure, and enhanced energy efficiency in urban areas. By bridging the gap between theory and practice, this study provides practical insights that inform evidence-based decision-making, guide policy formulation, and support efforts towards sustainable development. A nuanced understanding of the relationships between economic uncertainty, digitization, and environmental sustainability contributes not only to academic scholarship but also to practical applications in the field.

Moreover, while this study provides valuable insights into the role of digitization and economic uncertainty in shaping environmental sustainability, it is essential to acknowledge its limitations. These limitations, however, present opportunities for future researchers to delve deeper into the subject matter and address areas that require additional investigation. 1. Research Limitations: (1) Given the focus on 28 Chinese provinces, caution should be exercised when generalizing the findings to other geographical regions or countries. To enhance the external validity of the research, future studies should expand the sample to include a more diverse range of regions. (2) The accuracy and reliability of the variables considered in the analysis depend on the availability and quality of the data. Enhancing the accuracy of future findings would entail incorporating more comprehensive and reliable datasets. (3) While the study employs econometric techniques to establish associations, establishing causality remains a challenge. Endogeneity issues, such as bidirectional relationships or omitted variable bias, could influence the interpretation of results. Employing advanced econometric methods, such as instrumental variable approaches or natural experiments, can help address endogeneity concerns and establish causal relationships more robustly. (4) The study captures the relationships between digitization, economic uncertainty, and environmental sustainability within a specific time period. However, contextual factors such as policy changes, technological advancements, and socioeconomic shifts may impact these associations. Future research should consider these contextual factors and explore the temporal dynamics to gain a more nuanced understanding of their interplay. 2. Research Directions: (1) Future research should investigate the underlying mechanisms and mediating factors that drive the relationships between digitization, economic uncertainty, and environmental sustainability. Analyzing the specific channels through which digitization influences environmental outcomes and identifying mediating factors that amplify or mitigate the impacts of economic uncertainty can provide deeper insights into the complex processes at play. (2) Examining the effectiveness of specific policy interventions aimed at leveraging digitization, mitigating economic uncertainty, and achieving environmental sustainability is crucial. Comparative studies conducted across different countries or regions can shed light on the contextual factors and policy approaches that contribute to successful outcomes in diverse settings. (3) Longitudinal studies tracking the dynamics of relationships over an extended period can assess the long-term effects of digitization and economic uncertainty on environmental sustainability. Additionally, integrating forecasting models can enable the prediction of future trends and anticipate the impacts of different scenarios, providing valuable guidance for policymakers. By addressing these research limitations and pursuing these research directions, scholars can advance their understanding of the intricate relationships between digitization, economic uncertainty, and environmental sustainability, ultimately contributing to evidence-based strategies for a greener and more sustainable future.