Low-carbon City Research Articles

Impact of dual pilot policies for low-carbon and innovative cities on the high-quality development of urban economies

<p style="margin-top:16px; text-align:justify"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Based on the panel data of 276 cities in China from 2005 to 2021, the article used a multi-period double-difference model to explore the impact of the dual-pilot policy of low-carbon cities and innovative cities on the high-quality development of urban economy. The study found that (1) the implementation of the dual-pilot policy significantly had significantly promoted the economic high-quality development of the pilot cities, and the dual-pilot policy had a more significant effect on the enhancement of urban economic high-quality development compared with the single-pilot. (2) The mechanism analysis showed that green technology innovation, industrial structure adjustment and entrepreneurial activity played a mediator role in the impact of the dual-pilot on the high-quality development of the urban economy. (3) The spatial effect showed that the dual-pilot policy not only promoted the high-quality economic development of the region, but also had a positive spillover effect on the high-quality economic development of the neighbouring regions. (4) The heterogeneity analysis showed that the dual-pilot policy promoted the high-quality economic development of the eastern cities, large-scale cities, and non-resource-based cities more strongly.This study enriches the research on the impact of policy synergy on high-quality economic development, and provides references and insights for the effective implementation of dual-pilot policies and the promotion of high-quality urban economic development.</span></span></p>

Effect of low carbon policy on residents’ health: Evidence from China

A good ecological environment is foundational to the survival and health of society. This study utilized data from the China Family Panel Studies to empirically examine the impact of implementing low-carbon cities on residents’ health. The results revealed that low-carbon cities enhanced residents’ health. Mechanism analysis revealed that the construction of low-carbon cities fosters residents’ health by improving air quality, expanding urban green spaces, encouraging residents to engage in physical exercise, and enhancing residents’ mental well-being. This research offers policy insights to guide governments in pursuing a sustainable development path that prioritizes both natural environments and residents’ health.

Shades of Green: The Impact of Greenwashing on Stock Price Crash Risk

Using a sample of Chinese firms during 2008-2023, we reveal a positive relationship between firms' greenwashing levels and subsequent stock price crash risk. The two-stage regressions based on the instrumental variable approach and the staggered DiD regressions based on China's low-carbon city pilot programs confirm that the relationship is causal. Mechanism tests suggest that firms resort to greenwashing to mask negative information about their sustainability and managerial opportunism, thereby increasing subsequent stock price crash risk. Heterogeneity analysis reinforces this mechanism, showing that the impact of greenwashing is greater for firms with stronger greenwashing incentives, less transparency, and weaker governance mechanisms.

LOCAL RESIDENTS’ WILLINGNESS TO DONATE FOR LOW CARBON CITY INITIATIVES IN TAIPING, PERAK: PROPOSED E-BIKE-SHARING PROJECT

The transport sector in Malaysia ranked second in the country's total energy consumption for 2021, thereby contributing to greenhouse gas emissions. Numerous cities strive to attain low-carbon urban environments. E-bikes, as environmentally friendly vehicles, have the potential to play a significant role in urban transportation by contributing to the creation of low-carbon cities. Taiping, Perak, has been recognised as one of the world's top 3 most sustainable cities for 2019. This prestigious accolade positions it as an ideal location for an e-bike project, further solidifying its commitment to maintaining its status as a sustainable city. This study seeks to evaluate the awareness and attitudes of Taiping residents towards e-bikes, determine the factors that influence their willingness to donate, and ascertain the average amount they are willing to contribute to the e-bike-sharing development project in Taiping, Perak. A survey was conducted with 385 respondents using the Contingent Valuation Method, employing stratified random sampling. The findings indicated a notable level of awareness and favourable attitudes towards e-bikes. Factors such as bid, age, education, income, and awareness impact the willingness to donate. On average, each person contributes RM10.40 towards the proposed e-bike-sharing project in Taiping. The discovery offered valuable insights to the Taiping Municipal Council regarding securing financial support and better understanding residents' perspectives on e-bike sharing.

Does environmental regulation affect corporate environmental awareness? A quasi-natural experiment based on low-carbon city pilot policy

The low-carbon city pilot (LCCP) policy, as an important measure for China to achieve its "dual carbon" goals, has attracted widespread attention regarding its implementation effects. However, no studies have yet focused on the impact of the LCCP policy on corporate environmental awareness. This study, based on data from Chinese A-share listed companies from 2007 to 2020, employs a triple difference model to evaluate the impact of the LCCP policy on corporate environmental awareness, filling the aforementioned gap in the literature. The results indicate that the LCCP policy can significantly enhance corporate environmental awareness, particularly in non-state-owned enterprises, enterprises in the growth stage, and those located in the eastern regions. The LCCP policy will compel corporations to enhance their environmental awareness by increasing the costs of environmental penalty and environmental governance. However, the analysis of economic consequences reveals that although corporate environmental awareness has improved, it has not directly translated into significant economic benefits or substantial environmental benefits. This may be indicative of a scenario where, in the short term, the cost effect of the LCCP policy outweighs the compensation effect. Nevertheless, the improvement in the quality of information disclosure can significantly mitigate these adverse economic consequences. This research not only enriches the academic outcomes related to environmental regulatory policies but also contributes to ensuring the effective achievement of green, low-carbon objective and the sustainable development of the economy and society.

New energy policy and new quality productive forces: A quasi-natural experiment based on demonstration cities

The impact of new energy policy on the new quality productive forces (NQPF) in developing countries still lacks empirical evidence. The new energy demonstration city policy (NEDCP), serving as a comprehensive energy strategy in China, holds significant importance in driving the transformation of energy structures and fostering the NQPF. Based on the panel data collected from 276 Chinese cities, this study employs the difference-in-differences approach to examine the impact of the NEDCP on the NQPF. The findings indicate a substantial enhancement in the NQPF as a result of the NEDCP. While the NEDCP has a short-term effect on the NQPF, the long-term effect is insignificant. When considering the removal of low-carbon city variables, the influence of NEDCP was significantly improved. The NEDCP indirectly improves the NQPF through urban innovation capacity, government fiscal support, and industrial structure upgrading. The human capital level positively moderates the NEDCP's effect on the NQPF. In contrast to cities reliant on natural resources, the NEDCP exhibits a significantly greater influence on augmenting the NQPF in cities that are not dependent on such resources. Cities with higher urbanization levels experience a more substantial positive impact on the NQPF from the NEDCP compared to cities with lower urbanization levels. Our findings have important theoretical significance and can provide practical guidance to help developing countries improve NQPF through new energy policies.

Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

Dynamic simulation of carbon emissions (CE) in megacities is crucial for regional carbon reduction management, however, limited simulation accuracy hinders its application in carbon reduction policies. An integrated modeling framework was developed based on high-resolution multi-source data to analyze the street-level carbon emissions in Wuhan from 2015 to 2030. First, we conducted principal components analysis on the 5 driving factors of carbon emissions (including point of interests, electricity consumption, gross domestic product, population, and nighttime light) to delineate single carbon emission character region (CECR). Then, a machine learning method was used to simulating CE and explaining the contributions of different CECRs. Multi-scenarios CE accountings also were conducted with CECR simulations and an improved cellular automata model. Results shows that: (1) CE in Wuhan showed strong aggregation during the historical period. The five CECRs formed a near-concentric circle. The changes of CECR reflected the enhancement of human activity. (2) CE gradually shifted from the central urban areas to the surrounding regions during the scenario period, showing significant spatial spillover effects. Administrative districts with lower CE density exhibited greater scenario variation in total carbon emissions, indicating a higher potential for carbon reductions. (3) The total CE of Wuhan (148.11 Mt) in 2030 is projected to increase by 42.6 % compared to 2015 in the baseline scenario, representing 105 % of the low scenario and 91 % of the high scenario. The growth rate of total CE in Wuhan significantly slows down (<1 %) under all scenarios. The high-resolution dynamic simulation of CE will provide an important scientific basis for low-carbon city management in China.

Can China's low-carbon city pilot policy facilitate carbon neutrality? Evidence from a machine learning approach

This study examines China's 2060 carbon neutrality goal using the Double/Debiased Machine Learning (DML) model to analyze the causal effects of the low-carbon city pilot (LCCP) policy on urban carbon neutrality in 277 Chinese cities from 2008 to 2017. A series of empirical studies and robustness tests show that the LCCP policy has promoted carbon neutrality in Chinese cities. Through causal mediation analysis, this study identifies potential mechanisms, including increasing green credit, stimulating green innovation, optimizing industrial structures, and strengthening environmental governance. The LCCP policy has a varied impact on cities, depending on their resource endowments, geographical locations, and sizes. Furthermore, this study develops a green development index to assess the causal impact of the LCCP policy on green development, suggesting that it does not hinder the city's economic growth but rather enhances its quality and sustainability. This study provides empirical evidence for regional environmental governance and improvement strategies and offers valuable insights into China's transition from prioritizing rapid economic growth to achieving high-quality development.

Predicting land cover changes and carbon stock fluctuations in Fuzhou, China: A deep learning and InVEST approach

Predicting the spatiotemporal dynamics of land cover and its carbon stock holds significant importance in guiding regional sustainable development, enhancing regional carbon stocks, and addressing global climate change. However, there is insufficient research on the quantitative relationships between various land cover types and carbon stock changes, as well as their future spatial predictions. Focusing on the core area of Fuzhou City, China, this study constructs a streamlined framework by coupling deep learning and the InVEST model to predict urban land cover and carbon stock changes in 2025 and 2035. The results show that: (1) The prediction model for land cover change has high applicability and can produce the simulated images with high accuracy. Impervious surface is expected to increase by 53 km2 in 2025 and 131 km2 in 2035 compared to 2020, resulting in considerable reductions in forest and cropland. (2) Carbon stocks of the study area are expected to decrease by 1.68 × 106t in 2035 compared to 2020 due to large amounts of high-carbon-density forests and croplands being converted into low-carbon-density impervious surfaces. (3) Multiple regression analysis reveals that forests have the largest impact on carbon stocks in the area, with a magnitude 5.25 times greater than impervious surfaces and 11.5 times greater than cropland, whereas impervious surfaces are the second most influential land cover type on carbon stock changes. Therefore, expanding forest areas becomes an essential initiative as forests could offset the carbon stock loss caused by impervious surface growth. This study provides scientific references for optimizing land-use planning and formulating policies for the development of low-carbon cities.

Can low-carbon city pilot policy boost tourism development? Empirical evidence from China.

As an environmental regulatory tool in achieving green development, the low-carbon city pilot policy (LCCP) considerably influence tourism enhancement. Using China's 261 prefecture-level cities, this paper empirically delve the implication of LCCP to tourism from two tourism dimensions of tourism revenue and tourism arrivals employing the time-varying DID methodology. Results empirically reveal that the LCCP contributes to boosting tourism development, and after undergoing numerous robustness tests, this finding stands. The "arrivals growth effect" of LCCP is stronger than the "revenue improvement effect." Moreover, the impact of LCCP on domestic tourism is more profound than on inbound tourism. Heterogeneity analyses show the boosting impact is more pronounced in third-tier and above cities and non-resource-based cities. Upon further testing its transmission mechanism, we discover that the LCCP raises tourism revenues and arrival levels through industrial structural upgrading and innovation capacity improvement. The study clarifies the theoretical mechanisms and policy effects of LCCP on tourism development, which is valuable for assessing the tourism effects of LCCP and other environmental regulations and exploring the expanding path of sustainable tourism development.

Neighbor's profit or Neighbor's beggar? Evidence from China's low carbon cities pilot scheme on green development

China has implemented three sets of low-carbon city pilot policies (LCCP), making it the world's most extensive low-carbon and green development program. Many studies have examined the impact of this policy on green development. However, studies rarely discuss spillover effects. This deficiency can lead to biased policy evaluations. This study employs a quasi-experimental approach to investigate the spillover effects of LCCP on the green total factor productivity (GTFP) of neighboring non-pilot cities and identify the underlying mechanisms. Using panel data from 283 cities in China spanning from 2004 to 2020, this study employs the time-varying difference-in-difference method. The empirical evidence suggests that LCCP can significantly enhance the GTFP growth of non-pilot cities located within 100 km, with an average annual increase of approximately 1.43%. Mechanism analysis indicates that increasing technological innovation and learning from the pacesetter play crucial intermediary roles in promoting GTFP improvements in neighboring cities. Furthermore, the spillover effects exhibit noticeable heterogeneity, particularly among cities in the eastern region, middle region, and large cities. These findings provide empirical evidence regarding the spillover effects of China's largest carbon pilot policies, contributing to a comprehensive assessment of policy impacts and offering fresh insights for climate policy tools.

Implementation Effect, Long-Term Mechanisms, and Industrial Upgrading of the Low-Carbon City Pilot Policy: An Empirical Study Based on City-Level Panel Data from China

The green, low-carbon transition is a broad and profound change. The low-carbon city pilot policy (LCCP) is one of the most important strategies in China, aimed at dealing with climate change and realizing the green, low-carbon transition. Therefore, a quantitative evaluation of the implementation effect of the pilot policy is conducive to better promoting low-carbon work in the pilot areas. Based on 283 cities in China from 2005 to 2021, this paper constructs a double-difference model to empirically test the implementation effect, long-term mechanisms, and industrial upgrading of the pilot policy. The results show that the LCCP significantly promotes high-quality economic development and employment stability. After a series of robustness and endogenic tests, the conclusions in this study are still valid. Further analysis of the findings in this paper shows that the pilot programs promote the optimization and upgrading of industrial structures. The mediating effect shows that the LCCP has established three long-term mechanisms: developing alternative industries, expanding the level of openness, and promoting innovation. The heterogeneity analysis shows that the pilot policy’s implementation effect is more significant in cities located in central and western regions, as well as in non-resource-based cities. This study proposes the continuation of the promotion and implementation of the pilot policy, an increase in financial funds and policy support, the need to strengthen the labor market’s adaptability and protection mechanisms, the adaptation to local conditions to promote policy implementation, and the need to step up efforts to publicize pilot policies.

The impact assessment of low-carbon city pilot policy on urban green innovation: A batch-time heterogeneity perspective

The Chinese government is actively implementing the Low Carbon City Pilot (LCCP) policy nationwide to fulfill its commitment to carbon peaking and carbon neutrality. One of the main objectives of the LCCP policy is to enhance green innovation in cities, and many publications have assessed its effectiveness. While previous studies have considered differences in city characteristics, they have not adequately analyzed the varying effects of LCCP policy on different batches of pilot cities. To address this gap, this study utilized a cutting-edge heterogeneity causal inference approach to evaluate the heterogeneity of LCCP policy effects across different batches. The findings indicate that the LCCP policy significantly promoted urban green innovation and green substantive innovation. However, its impact on green symbolic innovation was not statistically significant. Among the different batches of pilot cities, the second batch was most strongly affected by the LCCP policy, followed by the third batch. Further analysis reveals that the initial group of pilot cities, with their varying innovation capabilities, carbon emission levels, and government intervention, along with the negative spatial competition among the cities, resulted in a less effective implementation of the LCCP policy compared to the second and third batches. This study contributes to a more accurate assessment of the effectiveness of the LCCP policy by highlighting the heterogeneity across different batches. We recommend that cities committed to low-carbon growth through green innovation customize the design and implementation of their low-carbon policies and establish broader networks of city-to-city cooperation.

Towards sustainable development: assessing the effects of low-carbon city pilot policy on residents’ welfare

Towards sustainable development: assessing the effects of low-carbon city pilot policy on residents’ welfare

Nonlinear impact of automobile industry agglomeration on CO2 emission: Incorporating urban characteristics in China

Despite the increasing economic benefits generated by the cluster effect of the automotive industry, CO2 emissions have become a serious challenge in the “3060” dual-carbon context. There are significant uncertainties regarding the impact of automotive industry agglomeration on urban CO2 emissions, including quantifying impact thresholds and understanding the sensitivity of different agglomeration patterns. This study explores how automotive industry agglomeration impacts CO2 emissions across 278 Chinese cities, and uncover the impact patterns based on varying city characteristics. The results show that the technology agglomeration of the automobile industry exhibits a nonlinear “U-shaped” impact on CO2 emissions with a threshold value of 0.4267, while the scale agglomeration shows a nonlinear inverted “U-shaped” impact with a threshold value of 0.1206. In low-carbon demonstration cities, technology agglomeration has the most significant impact, with fluctuations ranging from −14.65% to 16.12%. In contrast, transitional cities are most affected by scale agglomeration, with a fluctuation range of −18.1%–11.19%. Besides, the results indicate that China's automobile industry development programs tend to relocate economic activities from highly agglomerated low-carbon demonstration cities to potential development cities with lower levels of agglomeration and other transitioning cities. Lastly, the results reconfirm that developing automotive industry development models based on cities' characteristics can enhance the environmental benefits of industrial agglomeration.

Booster or inhibitor: Diagnosing effects of low-carbon pilot city policies on urban resilience from the perspectives of heterogeneity, mechanisms and spillover

Low-carbon city pilot policies are crucial for addressing urban challenges, alleviating pressure on urban systems, and encouraging sustainable development and resilience. This study focused on Chinese cities to explore how low-carbon city pilot policies influence urban resilience. The impact of the policies on urban resilience was assessed using difference-in-differences, mediating effect, and moderating effect models, showcasing both the variation and mechanism of the effects. The results are as follows: First, the introduction of low-carbon city pilot policies has resulted in a 9.3% increase in urban resilience, with noticeable enhancements over time. Second, the influence of low-carbon city pilot policies on urban resilience demonstrates heterogeneity, particularly with more pronounced effects observed in the eastern and coastal regions, as well as in resource-based, priority, and large cities. Third, the implementation of low-carbon city pilot policies has bolstered urban resilience through the promotion of technological innovation, the transformation of industrial structures, and the improvement of energy composition, with green production having a beneficial moderating function. Fourth, low-carbon city pilot policies have a siphoning effect on urban resilience within 50 km, and a positive spatial spillover effect between 50 and 200 km, both of which diminish with increasing distance. Finally, the examination of the policy implications derived from the study's findings is addressed. This study has the potential to serve as a valuable reference for formulating urban sustainable development strategies.

The Application and Evaluation of the LMDI Method in Building Carbon Emissions Analysis: A Comprehensive Review

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals.

Evaluating coupling coordination between urban smart performance and low-carbon level in China’s pilot cities with mixed methods

The construction models of smart cities and low-carbon cities are crucial for advancing global urbanization, enhancing urban governance, and addressing major urban challenges. Despite significant advancements in smart and low-carbon city research, a consensus on their coupling coordination remains elusive. This study employs mixed-method research, combining qualitative and quantitative analyses, to investigate the coupling coordination between urban smart performance (SCP) and low-carbon level (LCL) across 52 typical smart and low-carbon pilot cities in China. Independent evaluation models for SCP and LCL qualitatively assess the current state of smart and low-carbon city construction. Additionally, an Entropy–TOPSIS–Pearson correlation–Coupling coordination degree (ETPC) analysis model quantitatively examines their relationship. The results reveal that smart city initiatives in China significantly outperform low-carbon city development, with notable disparities in SCP and LCL between eastern, non-resource-based, and central cities versus western, resource-dependent, and peripheral cities. A strong positive correlation exists between urban SCP and overall LCL, with significant correlations in management, society, and economy, and moderate to weak correlations in environmental quality and culture. As SCP levels improve, the coupling coordination degree between the urban SCP and LCL systems also increases, driven primarily by economic, management, and societal factors. Conversely, the subsystems of low-carbon culture and environmental quality show poorer integration. Based on these findings, this study proposes an evaluation system for smart and low-carbon coupling coordination development, outlining pathways for future development from the perspective of urban complex systems.

Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities

In response to global climate challenges, urban low-carbon transformation has become a critical strategy for sustainable development. This study constructs a theoretical model for urban low-carbon transformation using the multi-level perspective framework. We focused on three batches of low-carbon pilot cities in China and employed fuzzy set qualitative comparative analysis to investigate the transformation pathways and impact mechanisms during the periods 2010–2012, 2012–2017, and 2017–2019. The results indicate that none of the six antecedent conditions is necessary for urban low-carbon transformation. Initially, the transformation is primarily driven by a pathway led by low-carbon industries. In the mid-stage, two pathways emerge: one dominated by the combination of low-carbon industries and research and development (R&amp;D) human capital and another led by low-carbon consumption awareness and economic development levels. In the later stage, the influencing factors involve a combination across micro, meso, and macro levels, reflecting an increasingly diversified and intricate configuration. The regional industrial structure consistently plays a dominant role, while awareness of low-carbon consumption has grown over time. This study not only enhances our understanding of the underlying mechanisms but also provides practical policy recommendations for local governments to tailor their strategies for effective low-carbon transformation.

The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of urban socio-economic activities, urban land use changes, and urban morphology, neglecting the importance of the three-dimensional spatial structure of cities. This study takes 30 cities in East China as an example. By using urban building data and carbon emission datasets, four machine learning algorithms, BP, RF, CNN, and CNN-RF, are established to build a CO2 emission prediction model based on three-dimensional spatial structure, and the main influencing factors are further studied. The results show that the CNN-RF model performed optimally in both the testing and validation phases, with the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of 0.85, 0.82; 10.60, 22.32; and 2.53, 1.92, respectively. Meanwhile, in the study unit, S, V, NHB, AN, BCR, SCD, and FAR have a greater impact on CO2 emissions. This indicates a strong correlation between urban three-dimensional spatial structure and carbon emissions. The CNN-RF model can effectively evaluate the relationship between them, providing strategic support for spatial optimization of low-carbon cities.