Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty.

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

Impact of carbon emission trading system on green technology innovation of energy enterprises in China.

China proposed establishing a carbon emission trading market in its 12th Five-Year Plan to reduce carbon dioxide emissions through market mechanisms, promote the development of science and technology and help China become an environment-friendly country. To examine the impact of carbon emission trading on green technology innovation in Chinese energy enterprises, data from 1993 to 2020 were collected from 494 A-share-listed energy enterprises. Enterprises located in the pilot area of carbon emissions trading were assigned to the treatment group, while those in the non-pilot area were assigned to the control group. The propensity-score-matching method was utilized to match the treatment group with the control group, and the resulting samples were used as the actual sample data. The difference-in-differences method was then employed to assess the net impact of carbon emission trading and investigate its effect on green technology innovation in energy enterprises. This empirical study suggested that carbon emission trading has a positive impact on green technology innovation in energy enterprises, particularly state-owned ones. Larger enterprises are more willing to engage in green technological innovation than small enterprises. Furthermore, when faced with a carbon emission trading system, 'mature' companies tend to pay more attention to green technology innovation than younger enterprises do. This study puts forward policy measures for establishing a national-level carbon emission market in China in the future.

Public participation and outgoing audit of natural resources: Evidence from tripartite evolutionary game in China.

Public participation is essential to the success of ecological civilization. Whether public participation can effectively play a role in the outgoing audit of natural resources (OANR) is an important issue that remains to be explored. This paper uses the tripartite evolutionary game to explore the mechanism of the audit subjects, the leading cadres, and the public in the OANR. The research finds that there is a two-way linkage relationship between the audit subjects and the leading cadres. The audit subjects and the leading cadres affect the behavior strategies of the public in the indirect way and direct way, respectively. However, the public lacks the path to directly affect the other two subjects. The tripartite ideal audit model of "the audit subjects conduct due diligence audits, leading cadres perform duties, the public participate" cannot be realized. The external effect of the public's strategic choice is not enough to make the profit or loss of leading cadres change structurally and then change their behaviors. This paper demonstrates the reasons why the public cannot effectively participate in the OANR at the current stage from three aspects, which are the interpretation of the equations for replication dynamics, the particularity of the audit system, and the effectiveness of public participation. Three suggestions are put forward which are encouraging citizens' indirect participation in the OANR, disclosing information about the OANR, and improving citizens' awareness of the OANR. This paper has important guiding significance for other developing countries to promote public participation in natural resource auditing.

Evaluating the energy efficiency-enhancing potential of the digital economy: Evidence from China.

Improving energy efficiency can go a long way in helping China address environmental problems it currently faces and help deliver on its pledge of achieving carbon neutrality by 2060. At the same time, innovative production technologies based on digital solutions continue to attract significant attention, owing to their potential to provide environmentally sustainable development opportunities. This study explores whether the digital economy can improve energy efficiency by facilitating input reallocation and promoting better information flows. We rely on a panel of 285 Chinese cities for the period 2010-2019 and a so-called slacks-based efficiency measure incorporating socially undesirable outputs to obtain energy efficiency from the decomposition of a productivity index. Our estimation results demonstrate that the digital economy can promote better energy use efficiency. More specifically, a 1-percentage point increase in the size of the digital economy leads to an average increase of around 14.65 percentage points in energy efficiency. This conclusion still holds under a two-stage least-squares procedure used to mitigate endogeneity. The efficiency-enhancing impact of digitalization is heterogeneous and depends on factors such as resource endowment, city size, and geographical location. Additionally, our results suggest that digital transformation within a particular region has an adverse effect on energy efficiency in that region's neighboring areas due to negative spatial spillover effects. These negative spillovers outweigh the positive direct effect on energy efficiency that can be attributed to a growing digital economy.

Spatio-temporal efficiency of fiscal environmental expenditure in reducing CO2 emissions in China's cities.

Although market-based CO2 emission control measures (e.g., carbon tax and carbon trading market) have been deeply discussed, government-based measures have received limited attention. This has led to increased uncertainty regarding the formulation of targeted emission reduction policies. Using a unique dataset, the non-radial directional distance function, a proposed spatial meta-frontier analysis method, and the log t convergence model, this study comprehensively investigates the spatio-temporal trends in fiscal environmental expenditure efficiency (FE) and corresponding causes for in a case study for 106 Chinese cities over 2007-2019. The results show that city-level FE presented a slow upward trend at a relatively low level, with a clearly skewed distribution. The technology gap effect between city groups and the overall best production technology, and the efficiency gap effect within city groups were the main drivers widening the overall FE gap. Convergence analysis indicated that three convergence clubs of FE were found, which were distributed across the country. This study highlights that, when constructing fiscal environmental expenditure policies, the government should focus on balancing the regional gap of FE while comprehensively improving FE.

A risk measurement study evaluating the impact of COVID-19 on China's financial market using the QR-SGED-EGARCH model.

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis.

A new perspective to promote sustainable low-carbon consumption: The influence of informational incentive and social influence.

In the era of internet-based information, how to promote sustainable low-carbon consumption by residents through information incentives and social influence is a pressing question that needs to be solved urgently. This study develops an explanatory model to explain how information incentives and social influence affect sustainable low-carbon consumption by residents. Data were collected from residents by large-scale online surveys in China. Partial least squares (PLS) regression was used to evaluate the model in its theory-mediated model scope to make it better than multiple regression. The empirical results show that purchase behavior, daily use behavior, waste disposal behavior, and public participation behavior define sustainable low-carbon consumption behavior; information incentives and social influence are two important predictors for low-carbon consumption behavior; at the level of information motivation, emotional information has a greater impact on low-carbon consumption behavior than rational information; and at the level of social influence, the influence of peer imitation is greater than that of endorsements and social norms. This study provides interesting insights into the important role of information and social networks for promoting low-carbon consumption behavior. Finally, we propose an information-based guidance policy to promote low-carbon consumption behavior based on social influence.

Financial inclusion, renewable energy consumption, and inclusive growth: cross-country evidence.

The COVID-19 pandemic has affected the global economy to varying degrees. Coupled with the widening gap caused by the unbalanced distribution of resources, the sustainability and inclusiveness of economic growth have been challenged. To explore the influencing factors of the level of economic inclusive growth among different countries, we used the spatial Durbin model to analyze the relationship between financial inclusion, renewable energy consumption, and inclusive growth based on panel data of 40 countries from 2010 to 2020. The results indicate a spatial autocorrelation in inclusive growth; financial inclusion and renewable energy consumption both contributed positively to inclusive growth, while industrial structure upgrading played a negative moderating role between domestic renewable energy consumption and inclusive growth. The results of this study provide insights into achieving better inclusive growth and maintaining sustainable and balanced economic development. Based on this, policy recommendations such as expanding the coverage of inclusive finance, optimizing the energy structure, and changing the economic development model are put forward.

Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19.

The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.

The development of China's Circular Economy: From the perspective of environmental regulation.

While achieving rapid economic growth, the pressure caused by environmental pollution and resource depletion has increasingly become a bottleneck in China's economic development, making the development of a circular economy particularly important. The extant literature has not focused on the influence of environmental regulation on a circular economy performance. This study uses the metafrontier global direction distance function (Metafrontier-Global-DDF) super-efficiency data envelopment analysis (DEA) model to estimate the circular economy performance and decomposition values of circular economy growth rate in 286 prefecture-level cities in China from 2003 to 2018. It further tests the influences of environmental regulations on circular economy performance and its influencing mechanism. The results show that environmental regulation can play a linear role in promoting the performance of the circular economy, mainly through the "catch-up effect," while "innovation effect" and "demonstration effect" have not yet played an effective role. This study provides evidence for the performance evaluation of the circular economy in China and the relationship between environmental regulations and circular economy performance. The future development of a circular economy still needs the active development of circular economy technology in each city. The role of the "innovation effect" and "demonstration effect" in improving the performance of the circular economy should be further enhanced.

Energy market integration and renewable energy development: Evidence from the European Union countries.

Based on the panel data of 20 countries in EU during the period of 2007-2019, this paper study the effect of energy market integration (EMI) on renewable energy development (RED). We develop a general equilibrium model to explain how EMI affect the RED and the role of different mechanisms. The empirical results reports that the European EMI increased both the consumption and power generation of renewable energy, which proves a significant positive effect of EMI on the RED. In line with our expectations of theoretical model, our estimates show that the increase of renewable energy consumption is mainly due to the fossil energy cost increased, technology advancement and regional environmental regulation strengthening. And the fossil energy cost is the main driven force which plays a completely mediating role between EMI and RED. Furthermore, we also observe a negative effect of FDI and industry structure on RED.

Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992-2019 based on calibrated nighttime light data.

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992-2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992-2019 based on our calibrated nighttime light data.

Manufacturing transfer and environmental efficiency: Evidence from the spatial agglomeration of manufacturing in China.

Manufacturing transfer is an important factor in optimizing the spatial distribution of resources and promoting regional environmental efficiency. Based on the manufacturing data of 30 provinces in China from 2005 to 2017, the spatial Durbin model is used to investigate the impact of three types of manufacturing transfer and spatial agglomeration effects on environmental efficiency under the spatial weight matrix of economic distance. The results show that the improvement of environmental efficiency is inhibited by the transfer of labor-intensive manufacturing but facilitated by the spatial agglomeration of such manufacturing. The transfer of capital and technology-intensive manufacturing has no significant impact on environmental efficiency. Environmental efficiency is significantly improved by the spatial agglomeration of technology-intensive manufacturing but significantly inhibited by that of capital-intensive manufacturing. Third, the impact of three types of manufacturing transfer on environmental efficiency is analyzed from the perspective of regional heterogeneity. This paper puts forward relevant policy suggestions from the perspectives of manufacturing transfer, the agglomeration effect, so as to further improve the environmental efficiency.

Spatiotemporal carbon emissions across the spectrum of Chinese cities: Insights from socioeconomic characteristics and ecological capacity.

In-depth investigation of the spatiotemporal driver patterns of city carbon emissions is vital toward establishing carbon neutrality, as such knowledge would aid policymakers in formulating differentiated emission reduction policies. Through developing a unique carbon emission dataset and applying a spatiotemporal logarithmic mean Divisia index decomposition approach, we explored the spatiotemporal drivers of CO2 emission for diverse cities in China categorized by economic structure and population size during 2002-2018. The results highlighted GDP per capita and industrial structure as the most positive and negative drivers, respectively, with the former overweighing the latter before 2016. Furthermore, the between-group differences of cities categorized using population size were higher than differences within groups, implying evident heterogeneity of carbon emissions. Emission related to within-differences in net primary productivity (NPP) constitutes the largest contributing factor promoting carbon emission in megacities and highly industrialized cities, whereas NPP between-differences in agricultural carbon intensity are predominantly associated with inhibiting emissions in large and highly commercialized cities. We therefore suggest that policymakers should optimize the industrial structure in highly industrialized cities and develop carbon sequestration in cities with high vegetation coverage through fiscal transfer for achieving carbon neutrality.

Impacts of renewable electricity standard and Renewable Energy Certificates on renewable energy investments and carbon emissions.

Accelerating the development of renewable energy is seen as an effective way for achieving the goals of carbon peak and carbon neutrality. The polices of Renewable Electricity Standard (RES) and Renewable Energy Certificates (REC) play increasing and important roles in developing renewable energy. In this paper, we develop an analytical model to analyze the impacts of the interaction of RES and REC polices on the renewable energy investment levels of an electricity generation firm and the carbon emissions. Our analysis reveals several interesting insights. First, we find that the green tags price under REC policy has a non-monotonic effect on the renewable energy investment, which highly depends on the quota (i.e., the required percentage of renewable electricity consumption on total electricity consumption) under the RES policy. Specifically, when the quota in RES policy is set too high, an increase in the green tags price will increase renewable energy investment; otherwise it will reduce the electricity generation firm's incentive to invest in renewable energy. Second, we show that the green tags price also has a non-monotonic effect on the carbon emissions. Specifically, when the quota in RES policy is set small enough, an increase in the green tags price will decrease the carbon emission. However, when the quota in RES policy is high enough, an increase in the green tags price will increase the carbon emission.

Economic and intensity effects of coal consumption in China.

The increase in coal consumption and its impact on the environment has become a bottleneck that hinders sustainable development. This paper discusses the effect of economic growth and coal intensity on China's coal consumption during 2005-2017 using the Laspeyres index decomposition method. The decoupling of coal consumption from economic growth was examined in conjunction with the Tapio elasticity index, and the decoupling contributions of economic growth and coal intensity are further determined. The results indicated that economic growth drives an increase in coal consumption; however, the contribution rate declines gradually with decrease in economic growth rate in each province. Further research showed that the secondary industry is the main contributor to the increment, and the rapid development of tertiary industry increases indirect coal consumption. Coal intensity has a positive impact on curbing coal consumption, but it is not sufficient to offset the increment generated by the economic effect. Moreover, in each province, the curbing effect gradually decreased as the decline in coal intensity weakened in the secondary industry. Furthermore, coal consumption is weakly decoupled from economic growth over the long term, and the secondary industry will determine the future trend of decoupling.

Drivers and trajectories of China's renewable energy consumption.

Renewable energy is significant for addressing climate change and energy security. This study focused on the drivers of China's renewable energy consumption (REC) by an extended production-theoretical decomposition analysis and emphasized REC technical efficiency and technological change in 28 provinces during 1997-2017. We then projected China's REC to 2030 based on nine scenarios using a Monte Carlo simulation approach and specifically considering the impacts of the COVID-19 pandemic on the national economy. The decomposition results showed that economic growth and population scale generally contributed to an increase in REC at national and provincial levels over the period while the overall technical efficiency and technological change in REC played limited roles in prompting REC nationally. The projection results indicated that the target that generates 50% of its electricity from renewable energy sources for China, could be achieved by 2030 if enough actions are taken to accelerate renewable energy development. Finally, we provided policy proposals that support our findings.

How do energy prices affect economic environment under different price regulation policies?

Energy is an essential factor for the survival of industries. Energy can affect the industrial productivity related to the economic activities, and the fluctuation of energy price will influence the final energy consumption level. In this paper, we use the input-output price model to study the relationship between the fluctuation of energy price and the change of economic level under different price control scenarios. The results show that the energy price fluctuation has high conduction efficiency on the general price index in the non-price-regulated scenario. Comparing the simulation results obtained from different years, this paper found that the magnitude of price conduction effect is closely related to the proportion of energy consumption. When the energy price is regulated by the government, the conduction effect of the energy price is limited. The policy effectiveness of regulation is related to the extent of price volatility of energy sources located at the upstream of the production chain of the regulated object. Through the SVAR model, this paper also found that the conduction effect of energy price fluctuation has obvious hysteresis, and the lag period of conduction effect on PPI is longer than that on CPI.

What determines urban resilience against COVID-19: City size or governance capacity?

This study analyzed the effects of urban governance and city size on COVID-19 prevention and control measures. Based on real-time data in 276 prefecture-level Chinese cities, we used the ordinary least squares plus robust standard error strategy. It was found that: (1) despite the non-significant effect of city size, urban governance capacity was an important factor affecting the prevention and control of the COVID-19 pandemic; urban governance capacity was particularly significant in the late control of the pandemic, but not significant in the early prevention; for every unit increase of urban governance capacity, the number of recovered COVID-19 cases per capita increased by 2.4%. Moreover, (2) the influence mechanism of anti-pandemic measures in cities could be divided into the workforce, financial, and material effects, and their contribution rates were 26.15%, 32.55%, and 37.20%, respectively; namely, the effective/timely assistance from Chinese central government regarding the workforce, financial, and material resources in key pandemic areas and nationwide played a major role in pandemic control. Additionally, (3) cities with a high level of smart city construction were more capable of enhancing the pandemic prevention and control effect, indicating that smart city construction is conducive to enhanced coping with public crises.

Chinese provincial multi-regional input-output database for 2012, 2015, and 2017.

Global production fragmentation generates indirect socioeconomic and environmental impacts throughout its expanded supply chains. The multi-regional input-output model (MRIO) is a tool commonly used to trace the supply chain and understand spillover effects across regions, but often cannot be applied due to data unavailability, especially at the sub-national level. Here, we present MRIO tables for 2012, 2015, and 2017 for 31 provinces of mainland China in 42 economic sectors. We employ hybrid methods to construct the MRIO tables according to the available data for each year. The dataset is the consistent China MRIO table collection to reveal the evolution of regional supply chains in China's recent economic transition. The dataset illustrates the consistent evolution of China's regional supply chain and its economic structure before the 2018 US-Sino trade war. The dataset can be further applied as a benchmark in a wide range of in-depth studies of production and consumption structures across industries and regions.