How Can Climate-Resilient City Construction Drive Green Sustainable Innovation? Evidence from 260 Chinese Cities

Abstract

Building climate-resilient cities strengthens urban livability and sustainable development levels. This paper constructs a difference-in-differences model to examine the impact of the pilot policy for climate-resilient city construction (CRCC—CRCC is used uniformly in the following text to represent the policy) on green sustainable innovation, using panel data of 260 prefecture-level Chinese cities from 2009 to 2023. The results reveal that CRCC can significantly promote green sustainable innovation in Chinese cities. Additionally, CRCC promotes green sustainable innovation by increasing the level of informatization, improving green total-factor energy efficiency, boosting corporate ESG performance, and alleviating corporate financing constraints. Therefore, it is necessary to further strengthen the implementation and promotion of China’s climate pilot policy. Attention should be paid to optimizing the pathways through which the pilot policy affects green sustainable innovation. Differentiated regional policies should be implemented based on local conditions. A tripartite linkage mechanism involving the government, enterprises, and the public should be established to increase societal awareness and support for climate-resilient city construction.

1. Introduction

In recent years, the concept of green and sustainable development has gradually been introduced as a societal consensus for high-quality development [1]. Green transition has become a pivotal part that drives social progress, and green innovation has become an important aspect of achieving this [2]. The concept of sustainable innovation was introduced in the 1990s. Unlike traditional technological innovation, it emphasizes dynamic stability over time, cumulative reinforcement and lock-in effects at technological level [3], the continuity of both the innovation process and its outputs, and stable long-term growth in economic benefits. Therefore, for current urban development, enhancing the capacity for green sustainable innovation is key to unleashing economic momentum and promoting high-quality development. It is also a core task in contemporary innovation efforts. Extreme weather events are becoming more frequent; cities, as central hubs of human activity and carbon emissions, are facing increasingly severe climate risks to their infrastructure, energy systems, and industrial layouts. At the macro level, industrial systems characterized by green innovation technologies can enhance power grid resilience [4], improve resource utilization efficiency [5], and strengthen cities’ overall adaptive capacity to climate shocks [6]. At the micro level, the deployment of green energy schemes ought to achieve technical feasibility and economic rationality; meanwhile, a human-oriented approach that precisely responds to residents’ demands for basic quality of life should be prioritized, such as access to clean energy [7].

China has taken an increasingly prominent leading stance in the global green urban transition. As the world’s largest developing country and largest carbon emitter, China faces particularly acute climate risks amidst its rapid urbanization process. The resulting disasters, such as smog and extreme heat, continuously test the effectiveness of urban climate risk governance systems, making climate response highly urgent [8]. Since the 18th National Congress of the Communist Party of China, China has introduced numerous policy measures to address climate change, among which the “Notice on Carrying out Pilot Projects for Climate-Resilient City Construction (CRCC)” is particularly prominent. As a policy instrument to respond to climate change, CRCC promotes sustainable development at the operational level of urban infrastructure, energy consumption, and industrial layout. CRCC not only demonstrates China’s proactive efforts in implementing the Paris Agreement and the United Nations 2030 Agenda for Sustainable Development [9], but also offers a “Chinese solution” to countries around the world. In a world facing great uncertainties, China, through institutional innovation and local practices, has explored a development path that balances economic growth with climate adaptation. Its experience holds valuable implications for building a community with a shared future for mankind.

Can CRCC enhance Chinese cities’ capacity for green sustainable innovation? If such an effect exists, through which channels does it operate? Whether and how does the impact of CRCC generate heterogeneous characteristics? Answering these questions holds significant value for empowering green sustainable innovation. To this end, this study sets the following three objectives: First, this study utilizes panel data from 2009 to 2023 in 260 Chinese cities to establish a difference-in-differences (DID) model to investigate the impact of CRCC on urban green sustainable innovation. Second, it seeks to identify and examine the mechanisms through which CRCC influences urban green sustainable innovation? Third, this research attempts to further explore the heterogeneous effects of CRCC across different contexts. By achieving the above objectives, this study provides theoretical support and empirical evidence for the optimization of CRCC and their global dissemination.

2. Literature Review

Prior research focuses on urban resilience. It is evident that the implementation of CRCC significantly enhances urban resilience [10]. It promotes urban resilience through human capital development and investment in resilient infrastructure [11]. Particular areas have been investigated to understand regional variations in urban resilience, such as Xiong’an New Area [12] and Ningbo City [13]. In addition, the policy’s role in promoting environmental responsibility have been examined at corporate level [14]. Moreover, limited studies focus on the impact of climate change on food security, suggesting that many dimensions of food security are affected, including production, utilization, and stability, proposing that the transformation of agri-food systems addresses climate crisis [15].

Current studies on sustainable innovation predominantly centers on the firm level. Digital transformation has influences on firms’ sustainable innovation on a dynamic input–output basis [3]. In the field of green sustainable innovation, drawing on the theories of resource-based view and dynamic capabilities, firms enhance the continuity of their green innovation activities by improving information visibility and risk-taking propensity through digital transformation [16]. Recent evidence have found that corporate green sustainable innovation is positively influenced through resource compensation and heightened managerial environmental awareness [17]. In contrast, while extensive research has illustrated certain degrees of contribution, it remains limited. Only a few studies reported that low-carbon city pilot policies impacted urban sustainability through green technology innovation and improvements in green living standards [18].

In summary, existing literature has extensively explored resilience in the context of climate-resilient establishments. However, according to the goal hierarchy theory, resilience building is essentially an instrumental pathway toward achieving urban sustainability rather than an ultimate end in itself [19]. Future work should therefore focus on advancing China’s strategic transition from “resilient cities” to “sustainable cities.” Current studies on sustainable innovation are concentrated at the firm level, while urban insights remains lacking. In particular, theoretical frameworks and empirical investigations into urban green sustainable innovation require further expansion. Based on the above discussion, the marginal contributions of this paper are in three aspects. Firstly, while existing evidence has focused on corporate green sustainable innovation, this study extends the focus to urban green sustainable innovation, thereby enriching the analytical scope. Secondly, it provides mechanistic studies that CRCC promotes green sustainable innovation by increasing the level of informatization, improving green total-factor energy efficiency, boosting corporate ESG performance, and alleviating corporate financing constraints. Thirdly, CRCC significantly promotes green sustainable innovation in small- and medium-sized cities and in areas southeast of the Hu Huanyong Line, while triple difference analysis also shows that higher public environmental awareness has a promoting effect.

3. Policy Context and Research Hypotheses

3.1. Policy Context of China’s Climate Resilience Initiatives

China has gradually established a climate policy system with distinctive local characteristics. This demonstrates the nation’s institutional innovation and practical exploration in the field of climate governance. From a historical perspective, the evolution of relevant policies exhibits clear phased characteristics. As shown in Figure 1, it can be divided into three stages.

3.1.1. The Policy Incubation Period

In June 1992, the United Nations Conference on Environment and Development adopted Agenda 21, providing a strategic framework to coordinate environmental protection and cooperation. Similarly, China passed its own Agenda 21 in March 1994, incorporating climate adaptation into its conceptual framework for the first time. In 2004, following discussions by the State Council Executive Meeting, China identified improving energy efficiency and reducing greenhouse gas emissions as integral parts of its energy development. The 11th Five-Year Plan incorporated energy conservation and emission reduction into national planning, setting a target to reduce energy consumption per unit of GDP by approximately 20% and promoting adjustments in energy structure and the development of green industries. This marked a transition from the absence to the establishment of policy-making awareness.

3.1.2. The Strategy Deepening Period

In 2007, China became the first developing country to formulate and implement the National Climate Change Program, requiring local governments to develop regional climate action plans. This reflected an initial shift in policy implementation from the central government to local authorities. The 12th Five-Year Plan further strengthened climate response measures, introducing the concept of “green development” and setting a target to reduce carbon dioxide emissions per unit of GDP by 17%. It also promoted the establishment of low-carbon pilot cities. In 2013, the release of the National Strategy for Climate Change Adaptation marked the birth of China’s first dedicated strategy for climate adaptation. The document explicitly called for integrating climate considerations into urban infrastructure development and linking climate resilience to national security, laying the groundwork for the concept of climate-resilient cities.

3.1.3. The Urban Practice Period

The 13th Five-Year Plan identified “green and low-carbon development” as one of its five core development concepts, setting a target of reducing carbon dioxide emissions per unit of GDP by 18%. In February 2016, four ministries jointly issued the Urban Climate Change Adaptation Action Plan, which is the nation’s first comprehensive policy focusing on urban climate adaptation. It aimed to enhance cities’ overall adaptive capacity by deploying seven major actions and launching demonstration projects in 30 pilot cities. In August 2016, the Pilot Work Plan for Climate-Resilient City Construction was released, initiating the selection and evaluation of pilot cities. In November of the same year, as a leading advocate and early signatory of the Paris Agreement, China actively promoted its entry into force. In February 2017, the National Development and Reform Commission and the Ministry of Housing and Urban-Rural Development jointly issued the Notice on Launching Pilot Projects for CRCC, marking a new phase of experimental exploration in climate governance. The first batch of 28 pilot cities carried out differentiated policy trials to advance CRCC.

To visually present the spatial distribution of the 28 pilot cities, Figure 2 further shows the number of pilot cities in each province using a provincial-level color gradient. The map reflects the regional concentration of CRCC. Hainan has the darkest color and the largest number of pilot cities, while Guizhou, Xinjiang, Shaanxi, and Hubei rank joint second. Liaoning, Hunan, and Gansu are in the third tier. Anhui, Qinghai, Zhejiang, and Jiangxi are the moderate ones, followed by Jilin, Heilongjiang, Shanghai, Jiangsu, and Chongqing, as they have relatively small numbers of pilot cities. The remaining provinces have zero pilot cities. Overall, the pilot cities exhibits a typical feature of targeted breakthroughs and scattered distribution.

Figure 3 maps the distribution of the 28 pilot cities at the prefectural administrative level. It should be noted that the actual extents of three pilots are smaller than the mapped units: Kuerle City (within Bayingolin Prefecture), Akesu City (within Akesu Prefecture), and Xixian New Area (spanning parts of Xi’an and Xianyang). The distribution covers eastern, central, western, and northeastern parts of China. Pilot cities in or near provincial capitals are relatively concentrated, which facilitates climate adaptation efforts using existing resources. Pilot sites are also located in border regions and ecologically vulnerable areas. Moreover, some pilot sites are geographically close to each other. Overall, Figure 3 complements the provincial-level map by showing a layout logic that is function oriented and type diverse.

In March 2021, the outline of the 14th Five-Year Plan and Long-Range Objectives Through 2035 included CRCC in top-level design. In May 2022, the National Climate Change Adaptation Strategy 2035 was issued, identifying cities and human settlements as key components in enhancing the adaptive capacity of socioeconomic systems to climate change. The report of the 20th National Congress of the Communist Party of China, released in October 2022, explicitly called for building cities that are livable, resilient, and smart. In 2023, China selected a new batch of pilot cities for CRCC. The nation’s ministries jointly issued the Notice on Deepening Pilot Projects for CRCC, which sets the goal of selecting exemplary cities by 2025 and expanding CRCC to all cities by 2035, thereby comprehensively improving urban capacity to climate adaption.

3.2. Research Hypotheses

3.2.1. The Direct Effects of CRCC on Green Sustainable Innovation

The pilot policy for CRCC is vital to tackle escalating climate risks and urban adaptation. Through systematic institutional support and resource integration, these policies significantly improve cities’ capacity for green sustainable innovation.

CRCC incorporated environmental objectives into their long-term development strategies. This reinforced the long-term nature of climate adaptation actions, providing policy assurance for green innovation. Both fundings for green projects and technical resources are attracted, effectively lowering the costs and entry barriers for green technology commercialization. This encouraged firms to invest in green research and development (R&D), promoting their transition from high-emission productions to low-carbon alternatives. This shift empowered corporate resource efficiency and competitive advantages, creating a virtuous cycle of policy incentives and overall green innovation capacity.

As firms increased their investment in green R&D, they accumulated experience and knowledge to absorb learning-by-doing effects during production. This is the mid-term where technologies are accumulated. Unit costs gradually decreased, and the scale of green technology applications expanded. Therefore, firms become more market driven. Meanwhile, firms could engage in industry–university–research collaboration, leveraging the research capabilities and innovation resources of institutional power to compensate for their shortcomings in technological innovation [20]. This approach helped enhance their innovation capabilities and technological breakthrough [21].

In long-term views, CRCC reshaped perceptions of climate actions. The visibility of green infrastructure enhanced public awareness of green technologies, and such low-carbon lifestyles became common knowledge among the public. Public environmental demands influenced corporate environmental attitudes and green transformation; therefore, green R&D was motivated to grow [22]. Therefore, we proposed the following hypothesis.

H1. 

CRCC promotes green sustainable innovation.

3.2.2. The Indirect Effects of CRCC on Green Sustainable Innovation in Chinese Cities

CRCC enhances green sustainable innovation by enhancing green total-factor energy efficiency. Mandatory environmental regulations such as stricter environmental standards and emission caps, helped phase out outdated production capacity, raised entry barriers for energy-intensive firms, and compelled firms to adopt cleaner technologies. Furthermore, measures like tax incentives, bonds, and subsidies facilitated corporate green technologies [23,24,25]. They reduced costs and released resources, thereby harnessing investment in green R&D. This optimized energy consumption structure and production efficiency. By enhancing green total-factor energy efficiency, the innovation compensation mechanism is realized, enabling the pilot policy to promote green sustainable innovation.

CRCC enhances green sustainable innovation by improving informatization levels. Regarding data acquisition, many regions have established climate digital platforms, strengthening the intelligence of climate monitoring and ecological management [26]. Hence, it is reasonable that improvements in informatization could enhance the continuity of environmental dataflows and the efficiency of risk warning. For example, cross-departmental data-sharing platforms broke down information barriers and thus facilitated the exchange of multi-dimensional green information. By reducing institutional transaction costs—a logic of government governance—and optimizing information search and matching efficiency—a logic of market benefit—urban entrepreneurial vitality is enhanced [27]. In addition, higher levels of informatization helped to monitor environmental dynamics and strategies promptly. Firms can utilize the intelligent management systems to digitalize operations, identify emission-reduction opportunities, and accelerate the iteration of green technologies. Such empowerment significantly improved the allocation efficiency of innovation factors, promoting efficient green transformation for both cities and enterprises. By improving informatization levels, data full chain is empowered, enabling the pilot policy to promote green sustainable innovation. Based on this, we put forward hypothesis 2 as follows.

H2. 

CRCC promotes green sustainable innovation by improving green total-factor energy efficiency and informatization levels.

3.2.3. The Indirect Effects of CRCC on Green Sustainable Innovation in Chinese Firms

CRCC enhances green sustainable innovation by alleviating corporate financing constraints. In terms of information symmetry, CRCC enhanced green infrastructure, established climate information disclosure platforms, and standardized carbon accounting systems. This mitigated adverse selection and credit rationing issues, thereby broadened access to green financing. Apart from that, CRCC created a dual-driven framework that combines institutional constraints with incentives. According to signaling theory, the formulation and implementation of policies convey positive development signals to the outside world [28]. This anticipated lower expectations of environmental risks from financial institutions. Firms, upon receiving sustainability signals, are motivated to self-adjust and accelerate green transformation, thereby expanding financing channels for green innovation activities [29]. Regarding resource restructuring, investments of climate adaptation generate technology spillovers and industrial agglomeration, and thus the marginal costs of corporate green innovation can be reduced [30]. Furthermore, unified climate resilience standards converted investments into mortgageable specialized assets, strengthening their internal financing capacity. The synergistic effect of these three ways alleviated corporate financing constraints, providing financial support to develop green innovation technologies and upgrade equipment and green production. This enabled enterprises to undertake long-term green innovation projects, thereby driving green sustainable innovation.

CRCC reshaped market structures to promote corporate ESG performance. On the supply side, CRCC conveyed positive signals of sustainable philosophies to the market, encouraging massive investments in environmental, social, and governance aspects. This strengthened certainty in long-term green technology investments and the market supply of low-carbon technologies. On the demand side, CRCC deepened public awareness through eco-labels and carbon inclusion, and it fostered positive sentiments among the public and the recognition of low-carbon consumption behaviors [31]. The influence of media coverage on corporate environmental issues intensifies. Accordingly, this expanded the market demand for green innovative products [32], firms were motivated to invest in green R&D, protect their social image and brand reputation [33], actively improve their ESG performance, and disclose information to align with market preferences. This ultimately achieves a dynamic equilibrium in the green innovation market, promoting the enhancement of green sustainable innovation. Based on the analysis above, we drew hypothesis 3 as follows.

H3. 

CRCC promotes green sustainable innovation by alleviating corporate financing constraints and enhancing corporate ESG performance.

In summary, the theoretical framework is illustrated in Figure 4.

4. Research Design

4.1. Model Setting

The pilot policy for CRCC was launched in February 2017, and it has created favorable conditions for the application of difference-in-differences (DID) method. The strength of this approach lies in leveraging the exogeneity of policy shocks to alleviate endogeneity issues and avoid reverse causality interference [34]. Therefore, this study employs a DID model to examine the policy effect on green sustainable innovation. All data processing and model estimation are conducted using Stata 18 software. The benchmark model is constructed as follows.

$${Rsgi}_{it}={\propto }_0+{\propto }_1{CRCC}_{it}+{\propto }_2{X}_{it}+{\gamma }_t+{\theta }_i+{\epsilon }_{it}$$

(1)

Among these, i represents the city; t represents the year; Rsgi_it is the level of green sustainable innovation; CRCC_it is the policy variable for CRCC—a dummy variable is set to 1 if the city is on the list of pilot areas for CRCC and 0 otherwise, while a time dummy variable is set to 1 if the period is after the implementation of the CRCC and 0 otherwise; X_it represents control variables; γ_t represents time fixed effects; θ_i represents location fixed effects; and ε_it represents the disturbance. The coefficient α1 for the policy variable did_it is the treatment effect of CRCC. If it is significantly positive, then the effects of CRCC on green sustainable innovation can be proved.

4.2. Variable Specifications

4.2.1. Dependent Variable

The dependent variable is green sustainable innovation (Rsgi). The sustainability of innovative behaviors reflects the continuity and long-term trends of innovations. Considering that evaluating sustainable innovation as output is more effective, this study uses the number of green patent applications to measure the sustainable level of green innovation. The specific calculation method is as follows:

$${Rsgi}_{it}=Ln[{\displaystyle \frac{{\mathrm{N}\mathrm{g}\mathrm{p}\mathrm{a}}_{it}+{\mathrm{N}\mathrm{g}\mathrm{p}\mathrm{a}}_{i,t-1}}{{\mathrm{N}\mathrm{g}\mathrm{p}\mathrm{a}}_{i,t-1}+{\mathrm{N}\mathrm{g}\mathrm{p}\mathrm{a}}_{i,t-2}}}\times ({\mathrm{N}\mathrm{g}\mathrm{p}\mathrm{a}}_{it}+{\mathrm{N}\mathrm{g}\mathrm{p}\mathrm{a}}_{i,t-1})+1]$$

(2)

Among these, Ngpa_it represents the number of green patent applications in cities at the current period, while Ngpa_i,t−1 and Ngpa_i,t−2 represent the number of green patent applications at the previous period and the period before that, respectively.

4.2.2. Independent Variable

CRCC is the core explanatory variable. It is constructed based on the interaction of time and region, distinguishing whether cities implemented the pilot policy during the sample period. For pilot cities (the treatment group), the variable takes the value of 1 starting from 2017 onward and 0 before 2017; for non-pilot cities (the control group), the variable is set to 0.

Based on the list of 28 pilot cities specified in the “Notice on Issuing the Pilot Work Plan for Climate-Resilient City Construction,” and considering data availability, five cities (or districts) were excluded: Korla City in Xinjiang Autonomous Region, Shihezi City in Xinjiang Production and Construction Corps, Aksu City (Baicheng County) in Xinjiang Autonomous Region, Qingyang City (Xifeng District) in Gansu Province, and Xixian New District in Shaanxi Province. Additionally, following the approach of Shi et al. [35], since only Bishan District and Tongnan District in Chongqing were designated as pilot areas, Chongqing was excluded to prevent estimation bias. After screening, this study includes the following 21 pilot regions as the treatment group: Hohhot City in Inner Mongolia Autonomous Region; Dalian City and Chaoyang City in Liaoning Province; Lishui City in Zhejiang Province; Hefei City and Huaibei City in Anhui Province; Jiujiang City in Jiangxi Province; Jinan City in Shandong Province; Anyang City in Henan Province; Wuhan City and Shiyan City in Hubei Province; Changde City and Yueyang City in Hunan Province; Baise City in Guangxi Autonomous Region; Haikou City in Hainan Province; Guangyuan City in Sichuan Province; Liupanshui City and Bijie City (Hezhang County) in Guizhou Province; Shangluo City in Shaanxi Province; Baiyin City in Gansu Province; and Xining City (Huangzhong County) in Qinghai Province. The remaining 239 prefecture-level cities serve as the control group.

4.2.3. Control Variables

To exclude interference from other variables on green sustainable innovation, this study selects the following control variables at the urban level. Foreign direct investment (lnFdi), measured as the logarithm of the actual utilized foreign capital converted into RMB using the annual average exchange rate. Market size (Mar), represented by the ratio of total retail sales of consumer goods to regional GDP. Degree of government intervention (Dgi), measured as the ratio of local fiscal general budget expenditure to regional GDP. Infrastructure level (lnInfras), represented by the logarithm of urban fixed asset investment. Industrial structure (Stru), measured as the ratio of added value from the tertiary industry to that of the secondary industry. Cultural resources (lnCult), measured as the logarithm of the number of public library books per person.

At the firm level, the following control variables are selected. Ownership concentration (Fsr), represented by the shareholding ratio of the largest shareholder. Firm size (Size), measured as the natural logarithm of total assets at year-end. Profitability (Roa), measured as the ratio of net profit at year-end to total assets at year-end. Financial leverage (Lev), measured as the ratio of total liabilities at year-end to total assets at year-end. Proportion of independent directors (Board), measured as the ratio of the number of independent directors to the total number of directors. Book-to-market ratio (Bm), measured as the ratio of total assets to firm market value. Duality of roles (Dual), assigned a value of 1 if the chairman also serves as the general manager, and 0 if otherwise. Institutional ownership ratio (Io), calculated as the proportion of shares held by institutional investors to the total number of company shares. Definitions and measurement methods for all variables are reported in

Table 1. Variable definitions and measurement methods.

Category	Variable	Definition	Measurement Method
Dependent variable	Rsgi	Green sustainable innovation	As shown in Formula (2) above.
Independent variable	CRCC	Construction of climate-resilient cities	Assigned a value of 1 if the year is 2017 or later and the region belongs to a pilot area, otherwise 0.
Urban variable	lnFdi	Foreign direct investment	Measured as the logarithm of the actual utilized foreign capital converted into RMB using the annual average exchange rate.
	Mar	Market size	Represented by the ratio of total retail sales of consumer goods to regional GDP.
	Dgi	Degree of government intervention	Measured as the ratio of local fiscal general budget expenditure to regional GDP.
	lnInfras	Infrastructure level	Represented by the logarithm of urban fixed asset investment.
	Stru	Industrial structure	Measured as the ratio of added value from the tertiary industry to that of the secondary industry.
	lnCult	Cultural resources	Measured as the logarithm of the number of public library books per people.
Firm variable	Fsr	Ownership concentration	Represented by the shareholding ratio of the largest shareholder.
	Size	Firm size	Measured as the natural logarithm of total assets at year-end.
	Roa	Profitability	Measured as the ratio of net profit at year-end to total assets at year-end.
	Lev	Financial leverage	Measured as the ratio of total liabilities at year-end to total assets at year-end.
	Board	Proportion of independent directors	Measured as the ratio of the number of independent directors to the total number of directors.
	Bm	Book-to-market ratio	Measured as the ratio of total assets to firm market value.
	Dual	Duality of roles	Assigned a value of 1 if the chairman also serves as the general manager, and 0 otherwise.
	Io	Institutional ownership ratio	Calculated as the proportion of shares held by institutional investors to the total number of company shares.

.

4.3. Data Sources

This study utilizes panel data from 260 prefecture-level cities across 29 provinces in China from 2009 to 2023. Data from A-share listed firms are also employed, with ST, *ST, and PT firms removed. A total of 1134 listed firms are retained, including 94 registered in cities located within pilot free trade zones. Missing values are addressed using linear interpolation. The data originate from the China City Statistical Yearbook (https://data.cnki.net, accessed on 15 May 2026), provincial statistical yearbooks (http://www.stats.gov.cn, accessed on 15 May 2026), and the CSMAR database (https://data.csmar.com, accessed on 15 May 2026). Green patents are sourced from the China Research Data Service Platform (https://www.cnrds.com, accessed on 15 May 2026). Descriptive statistics of the variables are presented in

Table 2. Descriptive statistics of the main variables.

Category	Variable	N Sample Size	Mean	Std. Dev	Minimum	Maximum
Urban variable	Rsgi	3900	10.0780	3.3753	0.6931	20.7713
	CRCC	3900	0.0377	0.1905	0	1
	lnFdi	3900	12.0306	1.9088	3.0775	16.9202
	Mar	3900	0.3819	0.1064	0.0489	0.9958
	Dgi	3900	0.1852	0.0814	0.0439	0.6876
	lnInfras	3900	16.4812	1.1699	10.1034	19.9877
	Stru	3900	1.0525	0.5921	0.1087	6.3874
	lnCult	3900	3.7274	0.9358	0.6931	9.3246
Firm variable	Rsgi	17,010	1.6094	2.6741	0	14.9028
	CRCC	17,010	0.0373	0.1896	0	1
	Fsr	17,010	35.3886	15.4884	3.3900	89.9900
	Size	17,010	22.8606	1.6338	19.0456	31.4309
	Roa	17,010	0.0384	0.0557	−1.0570	0.3840
	Lev	17,010	0.4849	0.2008	0.0103	1.0564
	Board	17,010	37.3219	5.7945	14.2900	80.0000
	Bm	17,010	0.6756	0.2709	0.0030	1.6360
	Dual	17,010	1.8277	0.3777	1.0000	2.0000
	Io	17,010	52.3358	20.9632	0.0006	98.7172

.

5. Results

5.1. Benchmark Results on City Data

The impact of CRCC on green sustainable innovation is shown in

Table 3. Baseline regression results at the city level.

Variable	Rsgi
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
CRCC	0.2819 ***	0.2661 ***	0.2783 ***	0.2693 ***	0.2948 ***	0.3095 ***	0.3122 ***
	(0.0838)	(0.0842)	(0.0842)	(0.0834)	(0.0803)	(0.0807)	(0.0808)
lnFdi		0.0552 ***	0.0570 ***	0.0475 ***	0.0131	0.0097	0.0084
		(0.0176)	(0.0176)	(0.0175)	(0.0177)	(0.0177)	(0.0177)
Mar			−1.4060 ***	−1.3644 ***	−1.4961 ***	−1.3329 ***	−1.3275 ***
			(0.2073)	(0.2082)	(0.2018)	(0.2012)	(0.2014)
Dgi				−1.2454 ***	−0.8756 **	−0.2101	−0.2057
				(0.4437)	(0.4429)	(0.4204)	(0.4199)
lnInfras					0.1906 ***	0.1840 ***	0.1837 ***
					(0.0254)	(0.0257)	(0.0257)
Stru						−0.3420 ***	−0.3390 ***
						(0.0602)	(0.0604)
lnCult							0.0352
							(0.0328)
Constant	10.0674 ***	9.4042 ***	9.9183 ***	10.2482 ***	7.5009 ***	7.8264***	7.7076 ***
	(0.0124)	(0.2119)	(0.2267)	(0.2497)	(0.4305)	(0.4379)	(0.4539)
City FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	3900	3900	3900	3900	3900	3900	3900
R2	0.9538	0.9539	0.9545	0.9546	0.9554	0.9559	0.9559

Note: figures in parentheses are standard errors; *** and ** indicate significant at the 1% and 5% levels, respectively.

, with columns (1) to (7) presenting the regression results. The empirical results indicate that the estimated coefficient for CRCC is significantly positive at the 1% level. As the control variables are progressively included, the estimated coefficient stabilizes at 0.3122, which remains statistically significant at the 1% level. This demonstrates that CRCC significantly promotes green sustainable innovation, thereby validating hypothesis H1.

5.2. Robustness Test

5.2.1. Parallel Trends Test on City Data

This study employs an event study methodology [36] to illustrate the parallel trends, as shown in Figure 5. The horizontal axis represents dummy variables based on the difference between the implementation year of the CRCC policy and the actual year, with endpoints adjusted accordingly. The vertical axis represents the dynamic policy effects. The test results indicate that, before the policy implementation, there is no significant trend difference between the treatment and control groups. After the policy implementation, green sustainable innovation is significantly positively influenced by CRCC.

5.2.2. Placebo Test

To further test the robustness of the results, this study constructs 1000 pseudo-policy dummy variables to conduct a placebo test. As Figure 6 has shown, the vertical dashed line marks the coefficient of 0.31, and the horizontal dashed line corresponds to a p-value of 0.1. The majority of the curve lies to the right of the vertical dashed line, indicating that the estimated coefficients generated by the counterfactual experiments significantly deviate from the true estimated coefficient of the actual policy in the real model. Furthermore, most p-values exceed 0.1, demonstrating that the randomly generated pseudo-treatment groups do not exert effects on green sustainable innovation, and thus validating the robustness of conclusions.

5.2.3. PSM-DID Test

To address potential selection bias, the propensity score matching combined with DID (PSM-DID) model is further employed, with kernel matching and radius matching adopted. As shown in Figure 7 and Figure 8, after matching, the absolute values of standardized biases for most covariates are less than 10%, indicating that the treatment group and the control group achieve a good balance in the distribution of covariates, and the matching effect is acceptable [37]. The results are presented in

Table 4. PSM-DID robustness test results.

Variable	Rsgi
	(1) Kernel Matching	(2) Radius Matching
CRCC	0.2790 *	0.2676 *
	(0.1478)	(0.1477)
Constant	7.7589 ***	7.7424 ***
	(0.9632)	(0.9639)
Control	Yes	Yes
City FE	Yes	Yes
Year FE	Yes	Yes
N	3528	3510
R2	0.9576	0.9580

Note: figures in parentheses are standard errors; *** and * indicate significant at the 1% and 10% levels, respectively.

. It is clear that the coefficient for CRCC remains significantly positive. This confirms the robustness of the results.

5.2.4. Endogeneity Test

Priority is often given to regions with stronger green development performance, and this brings endogeneity concerns. This study adopts the green coverage rate in built-up areas as the instrumental variable. Since the green coverage rate does not directly influence green sustainable innovation, it satisfies the exogeneity assumption of the instrumental variable. At the same time, regions with higher green coverage rates in built-up areas typically possess better microclimate regulation capabilities, which influences the formulation of CRCC, thus satisfying the relevance assumption. Furthermore, this study introduces the interaction term between the instrumental variable and yearly time dummy variables as the instrumental variable for empirical analysis to capture time-dimensional variation [38].

Column (1) in

Table 5. Endogeneity test results.

Variable	First Stage	Second Stage
IV	0.8734 ***
	(0.0014)
CRCC		0.3735 **
		(0.1765)
Control	Yes	Yes
City FE	Yes	Yes
Year FE	Yes	Yes
N	3598	3598
Kleibergen–Paap rk LM statistic		23.790 [0.0000]
Kleibergen–Paap Wald rk F statistic		370,000 {16.38}

Note: *** and ** indicate significance at the 1% and 5% levels, respectively; values in parentheses are standard errors; the under-identification test uses the Kleibergen–Paap rk LM statistic, with its p-value in brackets [ ]; and the weak identification test references the Kleibergen–Paap Wald rk F statistic, with the Stock–Yogo test critical value at the 10% level in braces { }.

presents the first-stage regression results, showing that the instrumental variable exhibits a significant positive relationship with the pilot policy at the 1% significance level and passes the weak instrument test. Column (2) presents the second-stage regression results, indicating that CRCC exerts a positive impact on green sustainable innovation.

5.2.5. Other Robustness Tests

(1)

Municipal-level cities are excluded. Since municipalities differ significantly from prefecture-level cities in many aspects, this study removes municipalities such as Beijing, Shanghai, and Tianjin to enhance comparability between the two groups. In particular, since Chongqing’s pilot areas are designated only as Bishan District and Tongnan District, and Chongqing is also excluded to ensure accuracy.

(2)

Other interference is excluded. The COVID-19 pandemic severely impacted the global economy. To avoid bias due to the inclusion of pandemic years, data from 2020 to 2022 are excluded.

Table 6. Other robustness tests results.

Variable	Rsgi
	(1) Excluding Municipal-Level City Samples	(2) Excluding the Impact of the COVID-19 Pandemic
CRCC	0.3051 ***	0.3010 ***
	(0.0809)	(0.0960)
Constant	7.6260 ***	6.6151 ***
	(0.4532)	(0.5814)
Control	Yes	Yes
City FE	Yes	Yes
Year FE	Yes	Yes
N	3855	3120
R2	0.9529	0.9541

Note: figures in parentheses are standard errors; *** indicates significant at the 10% levels, respectively.

shows that the coefficients remain significantly positive at the 1% level after excluding municipalities and pandemic-affected years, confirming the robustness of the findings.

6. Further Analysis

6.1. Tests Based on Enterprise Data

6.1.1. Benchmark Results on Enterprise Data

Micro-level evidence regarding the impact of CRCC on green sustainable innovation has been supplemented [39]. The results of the baseline regression are shown in

Table 7. Baseline regression results at the firm level.

Variable	Rsgi
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
CRCC	0.3857 ***	0.3916 ***	0.3528 ***	0.3562 ***	0.3609 ***	0.3589 ***	0.3625 ***	0.3630 ***	0.3624 ***
	(0.0866)	(0.0866)	(0.0845)	(0.0845)	(0.0846)	(0.0846)	(0.0844)	(0.0844)	(0.0844)
Fsr		−0.0052 **	−0.0081 ***	−0.0083 ***	−0.0082 ***	−0.0082 ***	−0.0077 ***	−0.0077 ***	−0.0083 ***
		(0.0021)	(0.0020)	(0.0020)	(0.0020)	(0.0021)	(0.0021)	(0.0021)	(0.0022)
Size			0.6571 ***	0.6550 ***	0.6794 ***	0.6802 ***	0.7098 ***	0.7096 ***	0.7010 ***
			(0.0299)	(0.0299)	(0.0320)	(0.0319)	(0.0340)	(0.0340)	(0.0356)
Roa				0.5987 **	0.3944	0.3926	0.2032	0.2004	0.1866
				(0.2346)	(0.2481)	(0.2481)	(0.2559)	(0.2562)	(0.2559)
Lev					−0.3095 **	−0.3040 **	−0.3370 ***	−0.3385 ***	−0.3290 ***
					(0.1255)	(0.1256)	(0.1261)	(0.1261)	(0.1268)
Board						0.0089 ***	0.0086 ***	0.0087 ***	0.0088 ***
						(0.0031)	(0.0031)	(0.0031)	(0.0031)
Bm							−0.2710 ***	−0.2711 ***	−0.2536 ***
							(0.0927)	(0.0927)	(0.0958)
Dual								0.0440	0.0444
								(0.0419)	(0.0419)
Io									0.0012
									(0.0015)
Constant	1.5950 ***	1.7795 ***	−13.1376 ***	−13.1065 ***	−13.5122 ***	−13.8605 ***	−14.3428 ***	−14.4205 ***	−14.2820 ***
	(0.0117)	(0.0765)	(0.6889)	(0.6890)	(0.7163)	(0.7248)	(0.7510)	(0.7527)	(0.7714)
City FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	17,010	17,010	17,010	17,010	17,010	17,010	17,010	17,010	17,010
R2	0.7206	0.7314	0.7315	0.7316	0.7318	0.7319	0.7320	0.7320	0.7321

Note: figures in parentheses are standard errors; *** and ** indicate significant at the 1% and 5% levels, respectively.

. Columns (1) to (9) show that the estimated coefficients consistently remain positive and significant at the 1% level, indicating that CRCC significantly promotes green sustainable innovation at the firm level.

6.1.2. Parallel Trends Test on Enterprise Data

As shown in Figure 9, the test results indicate that, before the policy implementation, there is no significant trend difference between the treatment and control groups. After the policy implementation, the DID model at the enterprise level does not reject the assumption of parallel pre-trends.

6.2. Results of Mediation Model

To test hypotheses H2 and H3 and explore the mediating role of green total-factor energy efficiency (Gfte), informatization level (lnInfor), corporate financing constraints (FC), and corporate ESG performance (ESG), this study refers to Jiang [40] to construct the following mediation model.

$$M_{it}={\beta }_0+{\beta }_1{CRCC}_{IT}+{\beta }_n{X}_{it}+{\gamma }_t+{\theta }_i+{\epsilon }_{it}$$

(3)

where M_it are mediating variables, namely green total-factor energy efficiency (Gfte), informatization level (lnInfor), corporate financing constraints (FC), and corporate ESG performance (ESG); and β₁ represents the coefficient of the explanatory variable. The definitions of other variables remain the same as above. Green total-factor energy efficiency (Gtfe) is measured using labor, capital, and energy as inputs; regional gross domestic product as the desired output; and industrial sulfur dioxide emissions, industrial soot and dust emissions, and industrial wastewater discharge as undesired outputs. The SBM index method is applied to calculate the green total-factor energy efficiency of each prefecture-level city [41]. The measurement framework is shown in

Table 8. Measurement of green total-factor energy efficiency.

Primary Indicator	Secondary Indicator	Tertiary Indicator
Input	Labor	Annual average number of employed persons
	Capital	Capital stock calculated using the perpetual inventory method
	Energy	Total energy consumption/ton of standard coal equivalent
Output	Desired output	Regional gross domestic product deflated using 2003 as the base period
	Undesired output	Industrial sulfur dioxide, soot and dust emissions, and wastewater discharge

.

Informatization level (lnInfor) is represented by the number of international internet users per 10,000 people. This indicator effectively captures both the infrastructure supply and social application outcomes of CRCC. It also bridges the transmission mechanism between adaptation actions and sustained green innovation. On the supply side, climate-resilient cities require high-level data monitoring systems. The realization of these functions depends on internet infrastructure. Improved network infrastructure directly increases internet penetration and user density in the region. On the application side, higher internet user density reflects the reach of environmental digital signals to micro-level agents. It also promotes open sharing of environmental data, reduces the application cost of green technologies, and extends the duration of the green innovation cycle.

To further explore the mechanisms through which green sustainable innovation may be influenced, two firm-level mechanism variables are included: the level of corporate financing constraint (FC) is measured using the FC index. Following Chen & Zheng [42], the index is constructed in several steps. First, firm size, firm age, and the cash dividend payout ratio are standardized on an annual basis. Second, based on the mean of these standardized variables, firms are ranked from high to low. The upper and lower terciles are used as cutoffs to define a dummy variable, FC_flag. Firms above the 66th percentile are classified as the low financing constraint group, and those below as the high financing constraint group. Finally, a Logit regression is estimated using Model (4). The fitted probability p-value for each firm-year observation is used as the FC index. FC ranges from 0 to 1, with a higher value indicating stronger financing constraints.

$$\begin{array}{cc}\hfill \mathrm{FC\_flag}={\mathsf{\delta }}_0& +{\mathsf{\delta }}_1\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}+{\mathsf{\delta }}_2\mathrm{L}\mathrm{e}\mathrm{v}+{\mathsf{\delta }}_3\left(\mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h}\mathrm{D}\mathrm{i}\mathrm{v}/\mathrm{T}\mathrm{A}\right)+{\mathsf{\delta }}_4\mathrm{B}\mathrm{M}+{\mathsf{\delta }}_5\mathrm{C\_Ratio}\hfill \\ & \hspace{1em} +{\mathsf{\delta }}_6\left(\mathrm{E}\mathrm{B}\mathrm{I}\mathrm{T}/\mathrm{T}\mathrm{A}\right)+\mathsf{\epsilon }\hfill \end{array}$$

(4)

In the above equation, Size represents firm size; Lev is the firm’s asset-liability ratio; CashDiv is the cash dividend paid by the firm in the current year; BM is the firm’s book-to-market ratio; C_Ratio is the firm’s current ratio; EBIT is the firm’s earnings before interest and taxes; and TA is the firm’s total assets.

Corporate ESG performance (ESG) is measured using the ESG composite score from the China Research Data Services Platform (CNRDS). The score is based on a three-level indicator system, which includes three primary indicators (environment, society, and governance) and 44 underlying data points. For qualitative indicators, firms receive scores based on whether they disclose the relevant information. For quantitative indicators, scores are assigned using industry-adjusted relative rankings. All indicators are standardized to a range of 0 to 100. The final composite score is obtained through a weighted aggregation using the analytic hierarchy process (AHP), with weights determined by expert evaluation. The result is an annual ESG composite score for each firm.

Column (1) in

Table 9. Mechanism test results.

Variable	(1)	(2)	(3)	(4)
	Gtfe	lnInfor	FC	ESG
CRCC	0.0280 **	0.0580 *	−0.0124 **	0.9024 **
	(0.0120)	(0.0332)	(0.0060)	(0.4031)
Constant	0.6008 ***	11.7031 ***	3.9783 ***	8.6008 **
	(0.0624)	(0.1620)	(0.0677)	(3.5439)
Control	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	3735	3900	12,330	17,010
R2	0.6953	0.9239	0.8768	0.6288

Note: figures in parentheses are standard errors; ***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively.

presents the impact of green total-factor energy efficiency on green sustainable innovation, showing a significantly positive effect at the 5% level. Column (2) shows that the impact of informatization level on green sustainable innovation, with a significantly positive effect at the 10% level. Therefore, CRCC significantly promotes green sustainable innovation by enhancing green total-factor energy efficiency and the level of informatization, thereby validating hypothesis H2. At the enterprise level, this study uses the degree of corporate financing constraints (FC index) and corporate ESG performance as mediating variables. Column (3) presents a significantly negative effect at the 5% level. This indicates that the CRCC significantly promotes green sustainable innovation by reducing corporate financing constraints. Column (4) shows a significantly positive effect at the 5% level. This demonstrates that corporate ESG performance serves as an important channel through which CRCC promotes green sustainable innovation, thereby validating hypothesis H3.

6.3. Heterogeneity Analysis

6.3.1. City Scale

Cities of different sizes exhibit significant differences in their levels of green sustainable innovation. cities with a permanent population of over 5 million are defined as large-scale cities, and those with fewer than 5 million residents are small- to medium-scale cities. A heterogeneity test is conducted [43], as shown in

Table 10. Heterogeneity analysis results.

Variable	(1)	(2)	(3)	(4)
	Small- and Medium-Sized Cities	Large Cities	Northwestern Side	Southeastern Side
CRCC	0.4273 ***	0.1641	−0.5848 **	0.3486 ***
	(0.0999)	(0.1294)	(0.2294)	(0.0893)
Constant	7.6699 ***	5.9391 ***	9.6738 ***	7.1872 ***
	(0.5226)	(0.7262)	(2.3308)	(0.4464)
Control	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	2567	1333	285	3615
R2	0.9161	0.9757	0.9400	0.9585
p-value for testing of between-group coefficient differences	0.079 *		0.050 *

Note: the test for between-group coefficient differences is calculated using Fisher’s combination test (with 1000 resampling iterations). Figures in parentheses are standard errors; ***, **, and * indicate significant at the 1%, 5%, and 10% levels, respectively.

.

Columns (1) and (2) in Table 10 indicate that the coefficient for small- and medium-sized cities is 0.4273 and significant at the 1% level, while for large cities, this is not significant. The Fisher combination test further supports this finding. This is because large cities exhibit weaker innovation momentum, with their complex urban systems, high transformation costs, and entrenched interests. Hence, emerging technologies are often overshadowed by the resolution of existing conflicts, and measures tend to stagnate at localized optimization, making it difficult to achieve breakthroughs. However, small- and medium-sized cities have more flexible organizational structures and higher decision-making efficiency, enabling the rapid integration of ecological concepts and infrastructure upgrades. These cities rely more heavily on natural resources and are more sensitive to ecological balance, so the policy has been more pronounced in these areas.

6.3.2. Geographic Location

As a demarcation line for human habitable areas, the northwestern side of the Hu Huanyong Line is characterized by vast territory and sparse population, with limited regional capacities. This locational disadvantage introduces a certain degree of heterogeneity in its impact [44]. Accordingly, a heterogeneity analysis based on the Hu Huanyong Line is conducted.

Columns (3) and (4) in Table 10 indicate that the coefficient for the northwestern side of the Hu Huanyong Line is −0.5848 and significant at the 5% level, while the coefficient for the southeastern side is 0.3486 and significant at the 1% level. The Fisher combination test further validates these findings. This suggests that CRCC significantly promotes green sustainable innovation on the southeastern side of the Hu Huanyong Line, whereas it significantly inhibits such innovation on the northwestern side. This is because cities on the southeastern side of the Hu Huanyong Line are economically dynamic, densely populated, and have well-developed infrastructure. Climate measures in these areas can effectively enhance the attractiveness of innovation factors and reduce the costs of ecological governance, thereby significantly promoting green sustainable innovation. On the contrary, the northwestern side tends to exhibit ecologically fragile and less economic agglomeration. Climate construction may worsen limited fiscal resources, exacerbate environmental pressures, and suffer from insufficient technological adaptability, leading to the suppression of local innovation vitality.

6.4. Triple DID Analysis

Currently, the public’s pursuit of a better ecological environment is growing increasingly strong, and their attention to environmental issues continues to rise [45]. As a crucial form of social supervision and market signaling, public environmental awareness can sustain continuous scrutiny over environmental issues, prompting local governments to introduce and strictly implement supportive incentive measures. This helps prioritize environmental governance appropriately, avoids “symbolic governance,” and thereby enhances the policy’s incentivizing effect on green innovation. Additionally, heightened public environmental awareness strengthens public preferences for green consumption and corporate intentions for green investment, reduces uncertainty in the green innovation market, and boosts enterprises’ motivation for long-term green research and development. Therefore, this study adopts public environmental awareness for triple DID analysis. The measurement of public environmental awareness and the construction of the triple difference-in-differences model are as follows.

$$\begin{array}{cc}\hfill {\mathrm{R}\mathrm{s}\mathrm{g}\mathrm{i}}_{\mathrm{i}\mathrm{t}}={\mathsf{\delta }}_0& +{\mathsf{\delta }}_1{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{p}\mathrm{u}\mathrm{b}\mathrm{l}\mathrm{i}\mathrm{c}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\delta }}_2{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{p}\mathrm{u}\mathrm{b}\mathrm{l}\mathrm{i}\mathrm{c}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\delta }}_3{\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{p}\mathrm{u}\mathrm{b}\mathrm{l}\mathrm{i}\mathrm{c}}_{\mathrm{i}\mathrm{t}}\hfill \\ \hfill & \hspace{1em}\hspace{1em} +{\mathsf{\delta }}_4{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\delta }}_{\mathrm{n}}{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\gamma }}_{\mathrm{t}}+{\mathsf{\theta }}_{\mathrm{i}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}\hfill \end{array}$$

(5)

where treat_it and time_it represent the regional grouping variable and the time grouping variable, respectively. Their product constitutes the DID interaction term; public_it denotes public environmental awareness. Drawing on Wu et al. [46], it is constructed using the average value of the Baidu Haze Search Index. A value of 1 is assigned if the index exceeds the average of 41.92087; otherwise, it is assigned 0. Its product with treat_it and time_it forms the triple difference-in-differences interaction term; δ1 is the coefficient estimate of primary interest in this section; and other variables refer to the previous descriptions.

Table 11. Triple difference-in-differences analysis.

Variable	Small and Medium-Sized Cities
CRCC × Public	0.2829 ***
	(0.1086)
Constant	8.6832 ***
	(0.4653)
Control	Yes
City FE	Yes
Year FE	Yes
N	3341
R2	0.9550

Note: figures in parentheses are standard errors; *** indicates significant at the 1% levels, respectively.

shows that the coefficient for the triple DID interaction term is 0.2829 and significant at the 1% level. This indicates that higher public environmental awareness can strengthen the incentivizing effect of CRCC on green sustainable innovation. This is because public environmental awareness, as an important informal environmental regulatory tool, can supervise and influence local government performance evaluations and the city’s positive image, thereby facilitating the smooth implementation of CRCC [47].

7. Discussion

7.1. Summary of Findings

The pilot policy for CRCC appears to be a notable initiative for promoting green sustainable innovation. To a large extent, it could help balance the dual goals of climate adaptation and green growth. The findings suggest that CRCC can contribute to green sustainable innovation. Several potential channels are identified, including informatization level and energy efficiency at the urban level, as well as financing constraints and ESG performance at the firm level. Heterogeneity related to city size and geographic location indicates that policy effects are influenced by local conditions, suggesting the need for differentiated strategies tailored to specific contexts. In addition, triple difference-in-differences analysis shows that public environmental concern plays a positive moderating role, which may help strengthen the innovation effects of CRCC. Overall, the proposed hypotheses receive empirical support from the findings. This study may thus provide a modest reference from China’s experience for other countries aiming to advance sustainability and climate adaptation.

7.2. Policy Effects

The study finds that CRCC appears to have a positive effect on green sustainable innovation. The outcome can be interpreted in light of China’s institutional, cultural, and governance context, and may also be compared with experiences elsewhere.

Institutional continuity matters. In China, ecological civilization has been priorities over the past decade, providing a stable environment for long-term green innovation [48]. Culturally, the traditional idea of “harmony between humanity and nature” may help foster public acceptance of climate policies. The moderating role of public environmental awareness observed in this study could reflect such cultural background. Rapid urbanization has also made climate adaptation a tangible concern for urban residents, which may have strengthened social support for climate action [49]. Governance structures also play a role. Large cities often face more administrative complexity and industrial legacy, which may constrain their transition, while small and medium-sized cities tend to have leaner structures and higher decision-making efficiency, where policy effects appear more pronounced [50].

Compared with other economies, China’s model has certain distinctive features. The European Union relies more on voluntary coordination, while China combines targets, fiscal incentives, and local accountability, which may lead to faster policy diffusion but also demands stronger implementation capacity. In some other countries, regional variations in climate action are evident, yet local innovation experiments there offer useful lessons. For many developing countries facing rapid urbanization, China’s efforts to integrate climate adaptation into urban planning, use digital technologies, and develop green finance provide a reference pathway based on domestic resource mobilization.

7.3. Recommendations

In light of these findings, the following recommendations are proposed: (1) Strengthen policy implementation and promotion. Governments should actively summarize and disseminate experiences from pilot cities, expand the scope of pilot programs, and encourage more cities to transition toward green innovation. (2) Emphasize pathway optimization for the impact of pilot policies on green sustainable innovation. Policies should prioritize supporting smart city development and the application of digital technologies, enhance green technology research and promotion, strengthen coordinated air pollution control, and reinforce carbon emission reduction. Additionally, a supportive system for corporate green innovation should be strengthened by improving green financial policies, alleviating corporate financing constraints, supporting environmental technology research and development, promoting green industrial upgrading, and advancing corporate environmental performance evaluations. (3) Implement differentiated regional policies tailored to local conditions. For large-scale cities, more flexible policies and streamlined approval processes are needed to gradually achieve holistic green upgrades. For small- and medium-sized cities, efforts should focus on consolidating advantages in green innovation, increasing fiscal and technological support, and optimizing regional innovation resource allocation. For cities on the southeastern side of the Hu Huanyong Line, technological innovation should be leveraged to foster innovation leadership in green industrial clusters. For cities on the northwestern side of the Hu Huanyong Line, increased fiscal support and ecological compensation should be provided, prioritizing low-cost, high-adaptability solutions to promote ecological security and livelihood improvements. (4) Establish a tripartite collaboration involving the government, enterprises, and the public. This can be achieved by disclosing environmental information, enhancing public awareness through science communication, guiding social capital into green innovation, and increasing societal support for CRCC.

7.4. Limitations and Future Research Directions

7.4.1. Limitations

Several limitations should be acknowledged: (1) The measurement of green sustainable innovation based on green patent applications, while commonly used, tends to focus on the quantity of innovations and offers limited reflection on their quality or long-term impact. Patent data may also be subject to certain institutional biases, such as strategic patent applications aimed at obtaining policy subsidies. (2) Regarding the potential lag in patent registration, this study constructed the green sustainable innovation indicator using a ratio that incorporates current-period and two-period-lagged green patent applications. This approach, to some extent, reduces reliance on a single time point and partially mitigates short-term biases associated with registration lags. (3) This study focuses on several observable mediating variables. There may be other unobserved channels, such as shifts in local political incentives, organizational learning within firms, or changes in intergovernmental fiscal relations. These are subject to the restrictions of current experimental conditions and remain to be explored in future research. (4) The sample period ends in 2023, which may not fully capture the long-term dynamic effects of climate-resilient city construction. Climate adaptation is by nature a long-term process, and the full benefits of green innovation may only become apparent over a longer time horizon.

7.4.2. Future Research Directions

Future research may be extended along several dimensions: (1) The measurement of green sustainable innovation could be further refined. Future studies may integrate dimensions such as patent citation quality, technology diffusion breadth, and environmental performance to develop a more detailed indicator system that captures quality aspects and long-term effects. (2) The micro-level mechanisms of policy transmission could be explored in greater depth. Case studies or field research methods may be used to analyze the implementation process in typical pilot cities, helping to reveal the specific roles of local political incentives, firm-level organizational learning, and government-business interactions in the transmission process. (3) The synergistic effects of multiple policies could be examined. Climate-resilient city construction is being implemented alongside other national strategies, such as the digital economy and the dual-carbon goals. Future research may investigate how these policies interact with each other, offering insights for optimizing policy portfolios. (4) Spatial econometric methods could be introduced. Green innovation activities may exhibit spatial dependence across cities. Future studies may employ spatial difference-in-differences or spatial panel models to incorporate inter-city spatial linkages into the analysis, allowing for a more refined identification of direct policy effects and potential spillover effects.

8. Conclusions

This study evaluates how CRCC impacts green sustainable innovation. The results indicate that: (1) CRCC significantly promotes green sustainable innovation. (2) It works by raising informatization levels and green total-factor energy efficiency. At the micro level, it also fosters innovation by easing corporate financing constraints and boosting ESG performance. (3) CRCC has a significant positive effect in small- and medium-sized cities and those on the southeastern side of the Hu Huanyong Line. (4) Triple difference-in-differences analysis reveals that CRCC significantly promotes green sustainable innovation through public environmental awareness.