Resource-based Cities Research Articles

Carbon emission scenario simulation and policy regulation in resource-based provinces based on system dynamics modeling

In China, numerous cities are resource-based, with substantial energy consumption, emissions, and pollution and they expand quickly and economically, notably enhancing their carbon emissions. Nevertheless, they have considerable potential for emission reduction. Assuming the “dual-carbon” goal as a backdrop, this study considered Shanxi Province, the largest coal resource-based province in China, for a case in point. It established a carbon emission system dynamics model, constructs five carbon emission systems, namely, economy, energy, population, land, and environment, and sets up four scenarios. Finally, in light of the scenario simulation's outcomes, we explored the optimal path and policy regulations for resource-based cities to reach carbon peaks. The study conclusions show the following: (1) All factors are exhibit correlated with each other, and their influence in the four scenarios is ranked as follows: GDP, energy consumption, industrial structure, total population, land-use structure. (2) Although GDP is a key factor influencing total carbon emissions, regulating only a single factor cannot achieve the carbon emission target. Thus, all factors need to be considered and synergistically regulated to achieve optimal carbon benefits. (3) Carbon emissions are higher and grow faster in the Baseline Development Scenario and the Fast Development Scenario, particularly in the FDS scenario, where they reach 614.48 million tons. Both scenarios exceed the peak carbon target by 4.4% and 7.1%. (4) In the Low-Carbon Optimization model and Resource Saving Scenario have low and slow-growing carbon emissions. Although the RSS has lower carbon emissions of 539.83 million tons, sacrificing sustainable development to reduce these emissions is unrealistic. In comparison, the LOS scenario represents the optimal path for achieving sustainable growth and lowering carbon emissions in Shanxi Province, with emissions totaling 550.99 million tons. This study implements strategies to manage the pace of population expansion, optimize the industrial structure, modify the energy structure, and optimize the allocation of land resources. The results of the study not only do our findings offer data reinforcement and implementation strategies for the low-carbon conversion of similar resource-based cities in China, but also offer case studies for different kinds of resource-based cities that fulfill the “carbon peak” objective.

Chinese cities show different trend toward carbon peak

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Can ecological compensation reduce air pollution? New evidence from resource-based cities in China

ABSTRACT China has made great efforts to establish an ecological compensation mechanism, but there lacks empirical evidence on whether this scheme effectively reduces air pollution. To test the effectiveness of air quality ecological compensation (AQEC) on air pollution control, this study considers 114 resource-based cities in China and uses a multi-period difference-in-difference (DID) model for empirical analysis. The finding shows that the AQEC policy significantly reduces the concentration of air pollutants by promoting air pollution prevention and local authority enthusiasm for pollution abatement, resulting in an average annual decrease in PM2.5 concentrations of approximately 3.9 µg/m3 in the pilot cities. The AQEC policy of resource-based cities in eastern and northern China, and those with less financial pressure have greater inhibitory effects on air pollution. The study recommends establishing long-term protection mechanisms and implementing differentiated policies focused on green technological innovation and financial autonomy.

Evaluating urban development in China's resource-based cities: a new perspective using nighttime light data

ABSTRACT Understanding the urbanization and transformation trajectories of resource-based cities (RBCs) is pivotal for China’s sustainable development goals. This study introduces a novel regression-based algorithm for assessing urbanization patterns. We delineated the urban boundaries of 335 cities and employed time-series nighttime light data from 2001 to 2020, shifting the analysis from a pixel-scale to an urban-scale perspective. Our analysis reveals distinct disparities within the 125 RBCs when compared to the national average, leading to their categorization. The key findings include: (1) Within Forest and Coal RBCs, numerous areas have stabilized after experiencing contraction. Geographically, a significant number of RBCs in the Northeast, North, and Northwest regions are experiencing or have experienced substantial urban shrinkage. The developmental status of RBCs exhibits a spatial positive correlation. (2) Although the government categorized RBCs based on their development level in 2013, findings suggest that this classification may no longer accurately reflect the current development status of RBCs in the context of urbanization. (3) Utilizing spectral clustering, we categorized RBCs into five types, identified 10 RBCs undergoing shrinkage and 30 cities trending towards stabilization post-shrinkage. This research offers a refined evaluative method for urbanization, providing insights beneficial for policy-making concerning RBCs’ sustainable growth..

Catalyst or Obstacle? Green innovation and total factor energy efficiency

Green innovation possesses dual externalities of “innovation” and “environmental protection”, and enhancing energy efficiency serves as a crucial means to promote high-quality economic development. Building upon the energy rebound effect, we use the balanced panel data of cities at prefecture level and above in China from 2008 to 2018 to explore the impact of urban green innovation on total factor energy efficiency (TFEE). The findings of this study indicate that, firstly, the impact of green innovation on TFEE exhibits a positive U-shaped pattern, characterized by initial suppression followed by subsequent promotion. This conclusion remains robust after undergoing a series of rigorous robustness tests. Second, high-quality green innovation is found to reach the turning point more quickly, implying that substantial green innovation can cross the turning point in smaller quantities. Thirdly, on the whole, in comparison to non-resource-based cities, resource-based cities are capable of reaching the turning point at an earlier stage. Finally, the new energy demonstration cities have not yet reached the turning point, while the non-new energy demonstration cities have crossed the turning point. This study contributes novel insights into the relationship between green innovation and TFEE, which holds significant implications for the formulation and implementation of sustainable development policies.

Digital economy, data resources and enterprise green technology innovation: Evidence from A-listed Chinese Firms

The digital economy provides a framework for leveraging data resources to enhance enterprise green technology innovation. By harnessing digital tools and analytics, businesses can optimize resource use, streamline operations, and develop eco-friendly products and services. This synergy between the digital economy, data resources, and green technology innovation underscores the transformative potential of technology in addressing environmental challenges and fostering sustainable development. Against this backdrop, this study examines whether the digital economy promotes green technological innovation and uses a two-way fixed effect based on the data of A-share listed companies in Shanghai and Shenzhen from 2011 to 2020. The mediation effect model and threshold model are used to explore the possible mechanism played by data resources. The study shows that the development of the digital economy significantly promotes enterprises' green technology innovation. This positive effect is especially prominent in the east-central region, second-tier cities, and non-resource cities. In contrast, the digital economy inhibits firms' green technology innovation in Tier 4 and Tier 5 cities and resource-based cities. Mechanism analysis shows that the digital economy can further promote enterprise green technology innovation by developing data resources. The threshold effect indicates a double threshold variable for data resources. The digital economy has differentiated effects on enterprise green technology innovation in different digital resource intervals. It provides a new perspective for understanding green technology innovation in the context of the digital economy and data resources.

Analyzing green and sustainable land use in China's coal cities: Insights from industrial transformation.

Driven by the goal of achieving sustainable development and carbon neutrality. Addressing environmental pollution and remediating land damage have become critical challenges in resource-based cities and regions with low land use efficiency. As a response, this study focuses on the 23 provinces where China's coal resource-based cities are situated. Utilizing data from 2014 to 2020, this research employs the SBM-Undesirable model, which considers undesirable outputs in efficiency calculations, and the Tobit regression test. It aims to explore the spatio-temporal variations in industrial transformation within resource-based cities and its impact on the efficiency of green space utilization. Furthermore, it analyzes the characteristics and the extent of the influence of factors such as industrial structure adjustments on urban land use efficiency, maximizing the output of land as a factor of production. The results show that: (1) Over the 7-year period studied, China consistently made nationwide adjustments to land area and land use structure to meet the needs of urban development (2) The regression test results show that the industrial transformation of resource-based cities can promote the improvement of green space utilization efficiency. The positive influence coefficient is 0.064 and is significant at a 1% level. (3) Environmental regulation, government expenditure, international trade, and green cover play a positive role in promoting green land use. The study provides valuable insights for policymakers and urban planners seeking to foster sustainable development in resource-based cities.

How can the digital economy drive low-carbon city performance in China to achieve sustainable development goals? A multiple-output perspective

Under the dual carbon target, governments globally have recognized the importance of exploring the relationship between the digital economy and low-carbon city (LCC) performance for achieving sustainable development. However, existing studies primarily focus on a single carbon emission index to reflect LCC performance while neglecting multidimensional outputs. This study aims to analyze how the digital economy drives LCC performance from a multidimensional output perspective. Based on 271 Chinese cities from 2006 to 2020, this study establishes a comprehensive digital economy index, and a multidimensional output index of LCC performance, then analyzes digital economy's impact on LCC performance through fixed effects and intermediary effects models. The findings indicate that: (1) Nationally, the digital economy has the potential to significantly decrease both overall and intensity of carbon emissions while increasing LCC efficiency. (2) Digital economy influences LCC development through innovation in green technology indirectly. (3) The digital economy's effects on LCC exhibits heterogeneity depending on city location, city size and city type. Specifically, it shows a greater effect on the decrease of carbon emissions in the eastern and central regions, exerts a bigger impact on small and medium-sized cities based on city size, and demonstrates a more substantial effect in resource-based cities. This study offers a thorough analysis of how the digital economy affects LCC performance, thereby providing empirical support for leveraging the digital economy's potential to foster urban low-carbon development.

Investigating the carbon curse of natural resource dependence: A carbon trading scheme

Fossil-based natural resources are carbon-intensive, and over-reliance on them often leads to persistent high carbon emissions at national or regional levels, resulting in the carbon curse. Based on the panel data of 114 resource-based cities in China from 2007 to 2020, this paper examines the carbon curse and solutions for these cities from the perspectives of total and intensity of carbon emissions. The results show that: Firstly, due to the different stages of urban development, resource endowment, degree of resource dependence, and policy background, there is a heterogeneity of carbon curse in China's resource-based cities. Secondly, the carbon emission trading policy can effectively mitigate the carbon curse in China's resource-based cities, and the robustness tests demonstrate the validity of this finding. Thirdly, under the carbon emission trading policy, industrial structure upgrading and digital transformation can play a secondary moderating role in resolving the carbon curse in resource-based cities, further strengthening the inhibitory effect of the carbon emission trading scheme on the carbon curse. In contrast, green technology innovation exhibits no enhancement in the impact of the policy. These findings contribute to the existing research on the carbon curse, offering valuable insights and policy implications for effectively addressing this challenge.

Enhancing carbon emission performance in resource-based cities through technological innovation under China’s digital economic development strategy

As indispensable contributors to the energy and resource sectors, resource-based cities have played a pivotal role in shaping China’s carbon emission landscape. The carbon emission performance of these cities directly impacts the overall carbon footprint of the country, thus highlighting the significance of enhancing carbon emission efficiency within resource-based urban areas to drive nationwide carbon reduction initiatives effectively. The digital economic development strategy presents a promising avenue for improving carbon emission efficiency in resource-based cities. This study employs the super efficiency epsilon-based measure model to assess the carbon emission performance of 103 resource-based cities over the period from 2011 to 2019. Additionally, it utilizes an intermediate effect model to analyse the impact mechanism of digital economic development on carbon emission performance. The findings reveal a substantial 6.3% enhancement in carbon emission performance in resource-based cities attributed to the implementation of the digital development strategy, primarily driven by technological innovation. Furthermore, the study identifies significant dual-threshold effects within economic growth and environmental regulation, shedding light on the complex interplay between these factors. These insights offer valuable guidance for regional carbon reduction endeavors and serve as a valuable supplement to the understanding of the environmental implications of digital economic development in resource-based cities.

Study on the Impact of Carbon Emission Trading Pilot on Green Land Use Efficiency in Cities

Under the overarching principle of sustainability, the reliance solely on expanding the landmass to meet the demand for high-quality economic growth is unsustainable. To address the need for harmonious ecological–economic development, this paper examines the influence of carbon emissions trading (CET) policies on the urban land green utilization efficiency (LGUE) from an environmental regulation perspective. Harnessing municipal panel data from 278 cities across China between 2011 and 2020, the study initially employs a super-efficient SBM model to estimate the urban LGUE. Further, a progressive difference-in-differences methodology is utilized to delve into CET’s impact on the LGUE. The main results are as follows. (1) Through the visual analysis of the time–space evolution trend, the LGUE displays pronounced spatial agglomeration, with the LGUE values being higher in the central and western regions compared to the east, and in the south versus the north. Over time, it follows a “U-shaped” change pattern. (2) The CET policy exerts a statistically significant positive influence on the LGUE, although this effect is accompanied by a temporal lag. Following a number of approaches to validate the results, the impact remains significant. (3) Regarding the heterogenous effects, the CET policy appears to have a greater impact on resource-based cities and those in the eastern part of China relative to non-resource-based and central–western cities. This research offers empirical evidence and countermeasure recommendations for the further refinement of the CET policy to enhance the urban LGUE.

Spatio-temporal heterogeneity and coupling effect of mining economy, social governance and environmental conservation: Evidence from Guangxi Zhuang Autonomous Region, China.

In order to solve the problem of coordinated development among mining economy, social governance and environmental conservation in global resource-based cities, we choose Guangxi Zhuang Autonomous Region as the research area. The advantage of resource endowment and resource industry was measured by location quotient and input-output method. The panel data related to mining governance from 2010 to 2021 were selected to build the evaluation and coupling analysis model between mining economic, social governance and environmental conservation, and the spatial-temporal heterogeneity and coupling effect of them were analyzed by comprehensive empowerment evaluation, spatial autocorrelation analysis and barrier degree methods. The results show that: (1) Except for the overall upward trend of social governance, the development level of mining economy and environmental conservation are basically stable; (2) The resource-rich areas have obvious mining economic advantages, and the central cities have good social governance capabilities, and the environmental conservation effectiveness is uncertain; (3) The coupling effect between mining economy and social governance is stronger than that between mining economy and environment conservation, and the synergistic coupling effect of the three is relatively random. Finally, we put forward some policy response strategies to Guangxi, and theoretical and practical reference would be provided for resource-based cities around the world.

A study on the impact of digital economy on green technology innovation of enterprises

<p>With global economic transformation and environmental sustainability increasingly taking center stage, the role of the digital economy (DE) becomes particularly critical in corporate green technology innovation (GTI). Based on the data of Chinese A-share listed companies in Shanghai and Shenzhen from 2011 to 2020, this paper explores the interactive effects of DE and GTI and the mechanism role of data resources (DR) in them, as well as conducts in-depth analyses for different regional and city characteristics. The main findings are as follows: First, DE significantly promotes the process of GTI. Second, from the perspective of regional differences, the promotion effect of DE on green technology innovation is particularly prominent in the eastern and central regions. And analyzed from the city level, its positive effect is most significant in the second-tier cities but shows a negative effect in the fourth and fifth-tier cities. Further examined from the perspective of resource dependence, DE inhibits green technology innovation in resource-based cities while showing a significant facilitating effect in non-resource-based cities. Third, DE can further promote green technology innovation by enhancing firms’ DR. Fourth, the study reveals the threshold effect of DR in this relationship: when <em>DR</em> < 2.5649, <em>DE</em> presents an inhibitory effect on green technological innovation, while its promotional effect begins to appear when 2.5649 ≤ <em>DR</em> < 4.2767, and the promotional effect of DE on GTI becomes more obvious when <em>DR</em> ≥ 4.2767.</p>

How Environmental Regulations Affect Green Total Factor Productivity—Evidence from Chinese Cities

Environmental pollution and resource waste in Chinese cities have become important obstacles to sustainable economic development, and it is urgent to change the mode of economic development and improve the quality of economic development. In response to this challenge, this study proposes environmental regulation as a solution and empirically tests the impact of environmental regulation on green total factor productivity. The empirical results show that environmental regulation can significantly improve urban green total factor productivity, the public environmental concern and green finance strengthen the positive effect of environmental regulation on urban green total factor productivity. The mechanism test shows that environmental regulation can improve urban green total factor productivity through green technological innovation and industrial structure upgrading. Heterogeneity analysis reveals that, compared to the resource-based cities, the positive effect of environmental regulation on urban green total factor productivity is more significant in the non-resource-based cities with relatively developed traditional finance and high levels of industrial modernisation. Compared to the central and western as well as the northeast regions of China, the positive effect of environmental regulation on urban green total factor productivity is more significant in the eastern region due to capital accumulation and technological constraints. The results of the study remain reliable after a series of endogeneity and robustness tests. These studies provide an important research basis for providing more targeted environmental regulation programmes and better improving green total factor productivity.

Ecological Safety Assessment and Convergence of Resource-Based Cities in the Yellow River Basin

Promoting the sustainable development of resource-based cities is of great significance for the ecological protection and high-quality development of the Yellow River Basin. In order to solve the dilemma of the sustainable development of resource-based cities in the Yellow River Basin, this paper constructs an ecological security evaluation system. It calculates the ecological security level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 using the TOPSIS model and carries out a classification convergence analysis. The DID empirical test of ecological security factors by sustainable development planning policies is used to distinguish the development characteristics of different resource-based cities. The results show that (1) the ecological security level of resource cities in the Yellow River Basin is generally well developed, and there are differences between different types of resource cities; (2) the resource cities in the Yellow River Basin mainly rely on industrial transformation to improve the ecological security level; and (3) the local governments pay limited attention to environmental protection.

Does industrial robot adoption affect green total factor productivity? – Evidence from China

This study investigates the impact of industrial robot adoption on Green Total Factor Productivity (GTFP) against the backdrop of increasing demand for both robot proliferation and green development utilizing urban panel data from Chinese cities spanning 2007–2019. Findings reveal that industrial robot adoption significantly improves GTFP, exerting its influence through mechanisms such as energy saving, technological innovation, scale output, and industrial linkages. Certain market factors moderate the influence of industrial robot on GTFP. Initially, regions reliant on high-pollution industries and resource-based cities experience minimal effects, which may diminish or even reverse as GTFP improves. Additionally, the positive influence of industrial robots on GTFP is particularly noticeable in regions with advanced development in green finance. This research is significant for understanding the connection between industrial robot adoption and green development at the urban level and for exploring pathways for green transformation.

A New Inversion Method for Obtaining Underwater Spatial Information of Subsidence Waterlogging Based on InSAR Technology and Subsidence Prediction

Surface waterlogging disasters due to underground mining and geological status have caused the abandonment of fertile land, seriously damaged the ecological environment, and have influenced the sustainable development of coal resource-based cities, which has become a problem that some mining areas need to face. However, the traditional underwater terrain measurement method using sonar encompasses a time-consuming and labor-intensive process. Thus, an inversion method for obtaining the underwater spatial information of subsidence waterlogging in coal mining subsidence waterlogging areas is proposed, based on differential interferometric synthetic aperture radar (D-InSAR) and the probability integral prediction method. First, subsidence values are obtained in the marginal area of the subsidence basin using D-InSAR technology. Then, the subsidence prediction parameters of the probability integral method (PIM) are inverted by a genetic algorithm (GA) based on the subsidence values. Finally, the underwater spatial information of subsidence waterlogging is calculated on the basis of the prediction parameters. The subsidence waterlogging area in the Wugou coal mine was adopted as the study area, and the underwater spatial information of subsidence waterlogging was inverted by the proposed method. The results show that this method can effectively provide the underwater spatial information of subsidence waterlogging, including the maximum subsidence value, waterlogging volume, subsidence waterlogging area, and underwater terrain in the subsidence waterlogging area. Compared with field-measured data from the same period, the RMSE of water depth is 99 mm, and the relative error is 9.9%, which proves that this inversion method is accurate and can meet engineering precision requirements.

Clean air policy and green total factor productivity: Evidence from Chinese prefecture-level cities

Clean air policy is an important mechanism for improving the urban environment and promoting green economic development. Green total factor productivity (GTFP) is an important way to measure these impacts. We apply the super-efficiency slack-based measure model, considering undesirable output, and the Global Malmquist-Luenberger index to measure the GTFP of 278 prefecture-level cities in China from 2005 to 2020. We find that GTFP growth is driven by both green technology progress (GTC) and green technology efficiency change (GEC). The traditional total factor productivity is overestimated, because energy and environmental factors are not considered. GTFP growth is higher in the cities in the eastern coastal regions compared to the western regions. We analyze the effect of clean air policy on GTFP using a difference-in-differences model and spatial-difference-in-differences model, and further conduct a heterogeneity analysis and mechanism analysis. The results show that clean air policy promotes GTFP growth. In addition, there is a significant negative spatial spillover effect with respect to the impact of clean air policy on GTFP. The heterogeneity analysis results show that the impact of clean air policy on GTFP growth is lower in resource-based cities and cities in the northern region, compared with non-resource-based cities and cities in the southern region. Further, the impact of clean air policy on GTFP growth is mainly achieved through GTC, GEC, innovation, and financial development. In resource-based cities and cities in the northern region, clean air policy decreases GTFP by inhibiting GTC, innovation, and financial development.

How Does the New-Type Urbanization Policy Affect Urban Low-Carbon Development?

The profound transformations brought about by the rapid advancement of urbanization have had a significant impact on low-carbon cities. In this study, we utilize the "New-type Urbanization Policy" (NTUP) as a quasi-natural experiment to examine the influence of NTUP on carbon emissions using the difference-in-differences (DID). Our findings indicate that NTUP effectively reduces carbon emissions and enhances emission efficiency, with consistent results even after conducting rigorous tests and addressing endogeneity concerns. Furthermore, our analysis reveals that resource-based cities, eastern cities and small- and medium-sized cities benefit more from NTUP in terms of reducing carbon emissions and improving efficiency. The mediating effect demonstrates that NTUP's impact on carbon emissions primarily operates through three mechanisms: promoting industrial structure upgrading, reducing energy use and promoting green technology innovation. This research offers valuable insights for achieving urban low-carbon sustainable development through new-type urbanization initiatives.