Low-carbon Technology Innovation Research Articles

Reducing carbon emissions in prefabricated buildings supply chains: a focus on component manufacturing processes.

The construction sector accounts for 23% of emissions from global economic activity, with China responsible for nearly 41%. Although China has vigorously promoted the development of prefabricated buildings (PBS) in pursuit of cleaner production, the carbon emissions from prefabricated component factories (PCF) should not be underestimated. So, the focus of this research was on how to promote the decarbonization of PCF. Based on the carbon trading market mechanism, the carbon emissions trading tax and revenue tax collection, the authors established a differential game model consisting of the local government and the PCF, studied the equilibrium solutions under different decision models, and analyzed the roles of the two tax systems, carbon trading revenue, and market preferences. The results are as follows: (1) The PCF's low-carbon technology (LCT) innovation efforts can be directly affected by the carbon price, component price, and tax rate and indirectly affected by influencing the local government's efforts. Besides, the local government and the PCF strategies can be changed through the central government's regulation of carbon prices and tax rates. (2) PCF should set reasonable prices for components, improving economic efficiency and the LCT stocks. (3) Cost-sharing contracts can encourage PCF to increase their LCT innovation, which is conducive to increasing the optimal benefits of the PCF. (4) The local government cannot be motivated by cost-sharing contracts. They can increase their optimal benefits only if the cost-sharing coefficient is below a threshold or if the environmental benefits from low-carbon production are above a specific value.

Research on Establishment and Application of Evaluation System of Urban Energy Strategy Development Indicators under the Perspective of Carbon Neutrality

A scientific, comprehensive and integrated assessment of urban energy development is of great significance for the establishment of a clean, low-carbon and efficient urban modern energy system. From the perspective of carbon neutrality, this paper sets 25 evaluation indicators in seven dimensions: energy supply, energy consumption, energy efficiency improvement, clean and low-carbon, safety and reliability, low-carbon transport, and scientific and technological innovation, and constructs a secondary indicator system for evaluating the strategic development of urban energy. The system adopts the hierarchical analysis method to determine the weights of the indicators, the double-baseline progression method to standardize the indicator scores, and finally the weighted composite index method to calculate the level of urban energy strategy development. This paper applies the index system to evaluate the current energy development status of Wenzhou city in 2020 and 2022, and to predict the energy strategy development in 2025 and 2030. The scores of Wenzhou city's urban energy strategy development level in the corresponding four periods are 63.56, 70.59, 77.87 and 85.06, indicating that by 2023, Wenzhou city's urban energy development level will go from medium development to high development. Wenzhou City should accelerate the proportion of renewable energy in the future. It is necessary to complement multiple energy sources and improve the integration of heat, electricity, gas and cold. In terms of end consumption, it is necessary to improve the efficiency of energy use, reduce energy intensity, implement electric energy substitution and form an energy consumption pattern centered on electricity.

Evaluating the Feasibility of Low-Carbon Technology in the Sustainable Economic Development of Electricity Using Generative Adversarial Networks

Abstract Electricity occupies a central position in energy strategy, and the low-carbon development of the power industry is an important guarantee for achieving sustainable development and carbon dioxide reduction goals. Orderly carrying out low-carbon technology transformation and innovation in electricity, ultimately achieving the goal of sustainable development of the power industry, has strong practical significance. Therefore, this article conducts a feasibility analysis of using generative adversarial networks to evaluate low-carbon technologies in the sustainable economic development of electricity. Firstly, the background of the theory of technological and economic evaluation was analysed and used as a theoretical basis. Secondly, an adversarial network was generated for the study of low-carbon emissions in the power industry, and an evaluation system for low-carbon technology in the sustainable economic development of electricity was constructed. The study showed that the preliminary economic evaluation index system and comprehensive benefit evaluation model for low-carbon technology transformation projects in electricity established in this article have feasibility and reliability for systematic analysis and evaluation of actual projects.

The Impact of Carbon Emissions Trading on the Total Factor Productivity of China’s Electric Power Enterprises—An Empirical Analysis Based on the Differences-in-Differences Model

Based on the panel data of China’s listed electric power enterprises, this paper adopts the differences-in-differences model to empirically analyze the pilot policy of carbon emissions trading’s impact on the total factor productivity of power enterprises in 2013. The study finds that the carbon trading pilot policy has a significant positive effect on the total factor productivity of power companies, and the two possible impact mechanisms are external cost compensation and additional income, and internal low-carbon technology innovation and resource allocation optimization. The conclusions above have been further confirmed by the parallel trend test and robustness test. The heterogeneity analysis demonstrates that there are differences in the regression results between state-owned enterprises and nonstate-owned enterprises. The possible reason is that state-owned enterprises are more likely to be affected by the carbon emissions trading system, and their asset-heavy model puts greater pressure on carbon emission reduction. Therefore, their demand for low-carbon technology innovation is more urgent; areas with stricter carbon emission verification are more sensitive to the implementation of carbon trading, and a reasonable increase in carbon verification can make the carbon trading market more effective. Based on the research results, this paper proposes to speed up the improvement of the national carbon trading market system, enhance the diversity and richness of the main market, improve the liquidity of the carbon trading market, broaden financing channels for electric power enterprises, and improve the carbon market supervision mechanism.

Decarbonization policy and high-carbon enterprise default risk: Evidence from China

Studying the impact of decarbonization policy on high-carbon enterprise default risk is of great importance to improve climate risk governance. However, the relationship between decarbonization policy and default risk has not been examined to date. Adopting data from Chinese A-share listed enterprises from 2000 to 2020, this paper examines the issue by using a difference-in-differences model. The results indicate that decarbonization policy significantly increases high-carbon enterprise default risk, which is more pronounced among small-sized enterprises and those in the growth period. The results of mechanism analysis show that the decarbonization policy increases the default risk of high-carbon enterprises by increasing debt cost and decreasing profitability. Furthermore, these effects are negatively moderated by low-carbon technology innovation and digital transformation. These findings can help governments, high-carbon enterprises, and investors to address default risk triggered by decarbonization policy.

Does environmental carbon pressure lead to low-carbon technology innovation? Empirical evidence from Chinese cities based on satellite remote sensing and machine learning

Climate change has become the most significant environmental issue facing human society in the 21st century, and a low-carbon transition is imminent. In order to comprehensively evaluate the balanced relationship between carbon activities and ecological environment under different regional scenarios. Opening up the black box of low-carbon technological innovation in Chinese cities under the pressure of climate change. This work combines the characteristic forecasting of random forest with the ordinary least square model to assess the contribution value and effects of various characteristic variables. The findings demonstrate that (1) China's carbon pressure level has changed from equilibrium to overload. The number of carbon high-pressure cities is increasing year by year. Carbon high pressure cities have risen from 12 to 68 in 20 years. (2) The carbon pressure on the city is directly influenced by the secondary industrial production value and the land urbanization ratio. Nevertheless, the level of environmental regulations imposed by municipal governments remains rather consistent, irrespective of the degree of urban carbon emissions. (3) Urban carbon pressure has a direct impact on the development of low-carbon technologies, with characteristic contributions of 0.91, 0.52, 0.43, and 0.3 at various pressure levels, respectively. (4) Population size and the level of economic development have a significant positive impact on the promotion of low-carbon technological innovation. The government should give full play to the talent and technology advantages of the core city clusters, and create a technological innovation alliance between the city clusters through the scale effect of economic factors. This study helps determine the correlation between carbon pressure and low-carbon technological innovation, and helps provincial and local governments identify their own carbon pressure status and choose differentiated emission reduction models.

Spatial pattern characteristics and optimization policies of low-carbon innovation levels in the urban agglomerations in the Yellow River Basin

This paper refines a comprehensive index of urban low-carbon innovation based on urban low-carbon innovation concept and an urban low-carbon innovation community structure and function. This index is complemented by an indicator reflecting equity in terms of urban-rural income gap, some indicators of low-carbon knowledge and technology innovation, as well as an indicator of the characteristics of a carbon circular economy, and a further refinement of the indicator of low-carbon innovation system. We use the entropy value method, kernel density analysis, gravity model, and social network analysis to study the low-carbon innovation spatial pattern in urban agglomerations (UAs) of the Yellow River Basin (YRB). Innovations in the findings are: (1) the low-carbon innovation level in the UAs in the YRB decreases in spatial distribution from the downstream to the midstream to the upstream UAs. (2) Reveal the spatial pattern laws of low-carbon innovation levels in different types of UAs in the YRB. The spatial pattern in regional level UAs is more complex and hierarchical than that in area level UAs; but there are still gaps in the hierarchy of spatial patterns of low-carbon innovation levels between UAs at the regional level, as well as between UAs at the area level. The spatial pattern of two regional-level UAs in the YRB is transitioning from a low-carbon innovation point-axis to a low-carbon innovation network structure, and the spatial pattern of one regional-level UAs is at the embryonic stage of the low-carbon innovation point-axis structure. The spatial pattern of one area-level UA is at the embryonic stage of a low-carbon innovation point-axis structure, and the spatial pattern of three area-level UAs is at the stage of low-carbon innovation pole. The spatial pattern of low-carbon innovation becomes more complex and hierarchical as we move from upstream to midstream to downstream UAs. (3) Use social network analysis and the study show that the low-carbon innovation network is dominated by the internal correlations among the UAs in the upstream, midstream, and downstream. The low-carbon innovation linkages between upstream, midstream and downstream UAs are weak. Finally, we put forward corresponding policy recommendations.

Evolutionary game analysis of low-carbon transformation and technological innovation in the cold chain under dual government intervention

Evolutionary game analysis of low-carbon transformation and technological innovation in the cold chain under dual government intervention

Can digital economy truly improve agricultural ecological transformation? New insights from China

AbstractAs the world’s biggest emitter of carbon, China’s agricultural carbon emissions account for 16 to 17%, with agriculture being the second largest contributor to carbon emissions. The development of the digital economy has brought profound changes to agricultural ecology. Therefore, we utilize China’s data experience to construct an agricultural ecological transformation system by setting up a Super-SBM model. Then, based on a dynamic panel threshold model, we explore the nonlinear impact mechanism of the digital economy on agricultural ecological transformation from a low-carbon innovation perspective. Our results reveal that the overall level of China’s agricultural ecological transformation is not high, but in the long-term posture, it represents a relatively steady upward trend, with a significant “wealth gap” between different regions. It is worth noting that there is a significant threshold effect of low-carbon innovation heterogeneity in the impact mechanism of digital economy on agricultural ecological transformation: under lower low-carbon innovation levels, the development of digital economy is unable to effectively promote agricultural ecological transformation. However, as the level of low-carbon technological innovation increases and exceeds the critical value, it stimulates the driving effect of digital economy to some extent, thereby promoting the improvement of agricultural ecological transformation, presenting a “U” shaped relationship. The paper has clarified the differential “new phenomena” in the process of promoting agricultural ecological transformation, providing new insights for achieving “carbon reduction and economic promotion” in developing countries.

Low-carbon mergers and acquisitions as a driver for higher energy efficiency: Evidence from China's high energy-consuming companies

Under the target of carbon peaking and carbon neutrality, high-energy consuming enterprises face enormous pressure to reduce carbon emissions. In order to quickly obtain low-carbon resources, low-carbon mergers and acquisitions (LMA) have emerged. This study attempts to test the impact of LMA on energy efficiency (ENE) in listed high energy-consuming companies based on SYS-GMM and fixed effects models. Results are as follows: (1) Compared to firms without LMA, firms with LMA increase ENE by 1.7 %. (2) Cash-paid LMA, horizontal LMA, and local LMA are more likely to promote ENE. (3) LMA promotes ENE by increasing environmental responsibility, low-carbon technology innovation, environmental investment. However, LMA may inhibit ENE by increasing manager confidence, management costs, and performance pressure. (4) The higher the government regulation and media supervision, the higher the promoting effect of LMA on ENE. (5) In the firm heterogeneity, LMA has more promoting effect on ENE in state-owned enterprises and eastern enterprises. This study is beneficial for providing references for high energy-consuming companies to promote LMA.

How do individual-level factors influence the adoption of low-carbon technology? Proposing and validating the bioeconomy technology acceptance model in the context of Africa

The bioeconomy seeks to efficiently transform biomaterials into value-added products to achieve circularity. A circular bioeconomy is a circular carbon economy based on bio-based resources. There is a dearth of information in the literature about how psychological factors affect public acceptance of the bioeconomy, especially in Africa, where the adoption of bioeconomy is scant. Addressing this gap, this study characterized bioeconomy as a low-carbon bio-based technological innovation to combat climate change and developed the Bioeconomy Technology Acceptance Model (BTAM) to explain the effects of individual-level factors on public acceptance of bioeconomy and investigated it in a survey (N = 465) using questionnaires that were carried out in Lagos, Nigeria, in 2022. The respondents were chosen by proportional stratified random sampling, and descriptive statistics, Pearson’s correlation coefficient, and structural equation modeling were used to analyze the data obtained. The strong influence of perceived usefulness from bioeconomy and intention to accept bioeconomy in BTAM suggests that the Technology Acceptance Model (TAM) is suitable for predicting public acceptance of bioeconomy. Considering the strong influence of belief about climate change on the perceived usefulness of bioeconomy and intention to accept it in this study, it is imperative to promote climate change education among Africans to accelerate acceptance of bioeconomy on the continent. The identified psychological factors provide a reference for scholars, policymakers, and manufacturers to effectively develop individual-oriented intervention strategies and promotion schemes to enhance acceptance of bioeconomy in Africa in particular and other climes where there is not yet widespread acceptance of circular bioeconomy.

Exploring the determinants of the evolution of urban and rural household carbon footprints inequality in China

Households are key actors in mitigating climate change and their carbon footprints are unequally distributed due to differences in the scale and patterns of consumption. This study separately estimates household carbon footprints inequality in urban and rural areas of China from 2012 to 2018 and identifies the factors contributing to the evolution of the inequality by applying environmentally extended multiregional input-output analysis, unconditional quantile regression and Oaxaca-Blinder decomposition. The results show that the largest emission growth occurred among the 50th–80th percentile of urban emitters and the highest rural emitters. The Gini coefficient of urban household carbon footprints decreased from 0.463 to 0.439, whereas that of rural household carbon footprints increased from 0.447 to 0.459. Notably, the changes of household characteristics exacerbated carbon inequality, with income growth, increased car ownership, and smaller household size being the three primary factors, while the changes in consumption preferences and technological advancements reduced carbon inequality. The latter are dominant in urban households and the former are dominant in rural households. To mitigate climate change and reduce inequality, the government should not only encourage high-emitting households to adopt greener low-carbon consumption and lifestyles, but also promote low-carbon technology innovation to reduce carbon intensity.

How does carbon emission trading scheme affect enterprise market value? A roadmap towards natural resources sustainability

China has committed to establishing a unified national carbon emissions market to achieve carbon reduction goals. The Carbon Emission Trading Scheme (CETS) internalizes emission reduction costs, which may affect enterprise market value (EMV), but this has seldom received attention. Therefore, based on A-share listed enterprises data from 2009 to 2019, this study not only focuses on investigating the direct and heterogeneous impact of CETS on EMV but also identifies the effective market mechanism design by constructing a unified research framework including carbon emission quota allocation schemes, penalty mechanisms, and EMV performance. The empirical results are as follows: (1) The CETS can significantly enhance the EMV of covered enterprises, indicating that China's CETS supports the Porter hypothesis. Compared with state-owned and small enterprises, the CETS has a more significant effect on promoting the EMV of non-state-owned and large enterprises. (2) The mixed carbon quota allocation scheme (the combination of free and auction schemes) is more effective in improving EMV than the pure free quota allocation system. Meanwhile, the mixed penalties combining economic and non-economic measures can effectively encourage CETS-covered enterprises to participate in carbon trading activities, thus enhancing overall EMV. (3) The results of the mediation effect test indicate that the mixed schemes improve EMV by promoting low-carbon technology innovation, and mixed carbon quota allocation schemes have a more significant effect through this channel. These findings have important reference value for improving EMV under the CTEs, achieving the carbon peak and carbon neutrality goals, and promoting sustainable economic development.

Spatial effects of innovation ecosystem development on low-carbon transition

In the context of innovation-driven development, the effective transformation of the low-carbon economy increasingly relies on the innovation ecosystem in which it exists. In order to explore the spatial effect of the innovation ecosystem on low-carbon transformation, this study comprehensively measures the innovation ecosystem and low-carbon transformation level based on China’s provincial panel data from 2015 to 2020 and use the spatial Durbin model to analyze the impact of the innovation ecosystem on low-carbon transition. The main conclusions are as follows: (1) There is apparent spatial heterogeneity in the development of innovation ecosystems, and the development pattern shows “cluster-type” agglomeration characteristics. However, the development level gap between provinces has remained the same. The southeast coastal economic zone has become an essential high-level development level of innovation ecosystems. (2) The innovation ecosystem has a significant spatial spillover effect on low-carbon transition. This conclusion still holds after multiple robustness tests. (3) In the process of low-carbon transition, the driving effect of the innovation ecosystem shows a “U” shaped stage characteristic of first decreasing and then increasing. (4) The impact of the innovation ecosystem on low-carbon transition varies within different economic circles. Spatial spillover has a boundary effect and the spillover peaks at 1200 km. This study provides theoretical support and empirical evidence based on China’s situation for transforming and developing a low-carbon economy. It provides important policy implications for accelerating low-carbon technological innovation, promoting sustainable development, and responding to climate change.

Research on Carbon Emission Characteristics and Differentiated Carbon Reduction Pathways in the Yangtze River Delta Region Based on the STIRPAT Model

With the changes in the spatial structure of China’s economic development, urban clusters have become the primary carriers of China’s regional economy and green growth. We used annual data from 2010 to 2021 to study the carbon emission characteristics and carbon reduction pathways of 36 cities in the Yangtze River Delta region. Firstly, based on the decoupling elasticity coefficient and carbon intensity index, the researchers divided the cities in the Yangtze River Delta into six types of carbon emissions. Then, the STIRPAT model was used to regress the panel data of different carbon emission types for 11 years, analyze the driving factors of carbon emissions, and develop differentiated carbon emission reduction paths for cities with six carbon emission types. According to the results, the cities of Type I need to accelerate low-carbon technology innovation; the cities of Type II need to improve energy efficiency and strengthen low-carbon technology research and development; the cities of Type V need to suppress foreign investment in high-energy consumption and high-emission projects in the local area; the cities of Type VI need to accelerate the process of new urbanization and optimize industrial structure. However, the researchers found that the cities of Types III and IV have not yet received effective emission reduction pathways, and their emission reduction policies and measures need to be further studied.

Government regulation, horizontal coopetition, and low-carbon technology innovation: A tripartite evolutionary game analysis of government and homogeneous energy enterprises

Low-carbon technology innovation (LCTI) is key to achieving energy conservation and emission reduction. As the main body of carbon emissions, accelerating the promotion of energy enterprises' LCTI is urgently needed. Based on the importance of horizontal coopetition and government regulation for enterprises' LCTI, this paper establishes a tripartite evolutionary game model of local government and two homogeneous energy enterprises to clarify their dynamic behavior strategies. Furthermore, through numerical simulation, a sensitivity analysis of the effect of the main parameter changes on all parties' evolution is carried out. Moreover, we obtained the accurate optimal value of S&P (subsidies and penalties) by using a combination of random jumping and L-BFGS-B method. The findings indicate that (i) increasing the initial willingness of government and enterprises can facilitate faster system evolution towards optimal state. Early governmental intervention can be beneficial in preventing negative behavior of enterprises. (ii) There exists optimal range, and even optimal level, for government S&P, with penalties being more effective than subsidies in promoting LCTI. (iii)Low coopetition is ineffective while the intensity of coopetitive relationship is positively correlated with its’ effect. (iiii) Small-scale enterprises are more sensitive to changes in S&P measures and coopetition relationships.

Assessing the carbon emission performance of digital greening synergistic transformation: evidence from the dual pilot projects in China.

Digital greening synergistic transformation (DGST) is a new engine for achieving dual carbon targets. Using panel data from 281 Chinese cities between 2003 and 2019, this study investigates the effect and transmission mechanism of the DGST on carbon emission performance. It is based on the quasi-natural experiments of the dual pilot projects "Broadband China" and "Low-carbon City",as well as the multi-period difference-in-difference method. The empirical findings are as follows: (1) DGST, represented by the dual pilot projects, effectively enhances carbon emission performance. Compared to the isolated "Broadband China" and "Low-Carbon City" pilot projects, establishing dual pilot projects generates a more pronounced carbon emission reduction effect through synergistic effects. (2) In terms of dynamic effects, the carbon emission reduction effect in dual pilot cities appears in the first year following initiation, indicating a certain temporal aspect to this effect. (3) Mechanism analysis reveals that the impact of DGST on carbon emission performance is channeled through two pathways: industrial structural adjustment and low-carbon technology innovation. (4) The positive impact of DGST on carbon emission performance exhibits heterogeneity, primarily present in cities with higher levels of financial technology, human capital, and new infrastructure development. It implies that the implementation of DGST necessitates adequate human, financial, and material support. In conclusion, this research contributes empirical evidence for further exploring policy synergies, offering support for achieving dual carbon targets through DGST.

The impact of political competition on low-carbon technology innovation: From the perspective of officials’ personal characteristics

Political competition is reflected in the strategic interaction of regional government's behavioral motivation driven by “official tournament.” Especially since environmental protection was officially included in the official performance appraisal system in 2005, the interactive trend of local governments’ economic development and environmental protection is more obvious. The policy uncertainty caused by this interaction is a major challenge for the government to promote regional sustainable development. Different from previous studies that only focus on the impact of political competition on environmental governance, this paper studies the impact of political competition on the low-carbon technology innovation from the perspective of sustainable development. Combined with the characteristics of Provincial Party Secretaries and Provincial Governors, the influence mechanism of political competition on low-carbon technology innovation is analyzed based on the method of spatial dubin model. Firstly, it finds that political competition has a significant negative impact on low-carbon technology innovation in the region and its adjacent regions, which is mainly caused by the political competition of Provincial Governors, while the political competition of Provincial Party Secretaries has no significant impact on low-carbon technology innovation. Secondly, the results of the mediation effect test show that reducing the intensity of environmental regulation in exchange for economic growth and political promotion opportunities is one of the reasons why the political competition of Provincial Governors inhibits low-carbon technology innovation. Thirdly, the results of the heterogeneity test of official characteristics show that the age of Provincial Governors is less and the tenure is longer, the stronger the inhibiting effect of Provincial Governors’ political competition on low-carbon technology innovation is. These conclusions provide policy suggestions for enterprises to make environmental investment decisions under different political environments.

Research on the Current Situation and Economic Development Trends of China's Energy Economy Structure: Taking Shandong Province as an Example

Shandong Province is a province with strong economic growth and high energy consumption in China. The energy consumption structure is mainly composed of coal and oil. While the regional GDP is increasing with each passing year, carbon emissions are also increasing year by year, and carbon emissions are mainly concentrated in high energy consuming industries. Coal plays a very significant part in the growth of social economy and has become a necessity for people's production and life. The coal based chemical industry is one of the six leading industries of Shandong Energy Group. The coal chemical industry is a significant component of China's energy construction, and how to improve the quality and efficient utilization of coal chemical products is currently the focus of close attention in the coal chemical industry. Promoting economic transformation and upgrading, as well as energy structure adjustment, is a necessary path for China to achieve the objective of "carbon peaking and carbon neutrality". Carbon peaking and carbon neutrality "is the main foundation and new engine for China's economic and social growth in the next 40 years, which will inevitably drive systematic changes such as low-carbon transformation, structural reshaping, and technological innovation in the energy industry. This article takes Shandong Province as an example to study the current situation of China's energy economy structure and economic development trends.

Evaluation and calculation of urban carbon emission reduction potential based on spatiotemporal geographic model

With the growth of urban carbon emissions, found that the current urban carbon emission reduction potential assessment and calculation in carbon emissions data source, research index selection and cost are some problems, to promote the realization of low carbon emission reduction target, it is necessary to use space and space and geographical weighted regression model to obtain the spatial and temporal distribution of urban carbon reduction. This article combined geographic information system (GIS) technology to construct a spatial database. Based on the geographic weighted regression (GWR) model, a time dimension was introduced to construct a spatiotemporal GWR model, which was used to obtain the spatiotemporal distribution information of urban CEs and draw a spatial distribution (SD) map of urban CE reduction potential (RP). This article also conducted experimental tests on the carbon reduction potential (CRP) of Chinese cities by combining different carbon reduction (CR) measures. This paper uses three CR methods to test P of these cities respectively measure A adjust industrial structure, measure B to promote low carbon technology innovation and measure C is to optimize the urban space structure, the experimental results show that the average carbon emission intensity of measure A cities is 0.72 tons / yuan, 0.02 tons / 0,0.04 tons / yuan less than measures B and C; the average CE efficiency of measures A cities is 0.85,0.02 and 0.05 compared with measures B and C respectively. From the above data, adjusting the industrial structure can effectively improve the CE efficiency of various cities, reduce the CE, reduce the CE intensity, and promote the realization of CE reduction in cities.