Energy structure, digital economy, and carbon emissions: evidence from China

As a new production factor, digitalization plays a vital role in society, economy, and the environment. Based on the expanded STIRPAT model, this paper empirically tests the impact of energy structure and digital economy on carbon emissions by panel data from 2011 to 2017 in 30 provinces of China. The results show that the energy structure mainly based on coal has a significant driving effect on carbon emissions. Compared with non-resource-based provinces, the increase of energy structure dominated by coal has a greater effect on carbon emission in resource-based provinces. It is worth noting that this kind of influence has a greater impact on the central region of China, followed by the western region and the eastern region. Besides, the digital economy has a significant moderating effect. With the development of digital economy, the impact of coal-based energy structure on carbon emissions is gradually decreasing. This effect is more significant in non-resource-based provinces and eastern China, but not significant in resource-based cities and central and western China.

The role of renewable energy technological innovation on climate change: Empirical evidence from China

To develop renewable energy as well as promote China's transition to a low-carbon economy, the government needs to pay attention to renewable energy technological innovation (RETI). Using China's provincial panel data from 2000 to 2015, and regarding the CO₂ emissions as the proxy of climate change, this paper identifies the relationship between RETI and CO₂ emissions as well as seeks to confirm the role of RETI on climate change. The linear regression model confirms that the RETI has a significant negative effect on CO₂ emissions. In addition, considering the disparities of energy structure, the impacts of RETI on CO₂ emissions may be distinct. We, therefore, construct a panel threshold model by taking into account the distinct effect of RETI under different energy structure. We find that the effect of RETI on curbing CO₂ emissions decreases with the rising of coal-dominated energy consumption structure but in contrast, this effect increases with the growing proportion of renewable energy generation. This paper provides new insight into the relationship between technological innovation and climate change. Based on these findings, some relevant policy recommendations are proposed.

Improved source apportionment of PAHs and Pb by integrating Pb stable isotopes and positive matrix factorization application (PAHs): A historical record case study from the northern South China Sea

To obtain the historical changes of pyrogenic sources, integrated source apportionment methods, which include PAH compositions, diagnostic ratios (DRs), Pb isotopic ratios, and positive matrix factorization (PMF) model, were developed and applied in sediments of the northern South China Sea. These methods provided a gradually clear picture of energy structural change. Spatially, Σ₁₅PAH (11.3 to 95.5ng/g) and Pb (10.2 to 74.6μg/g) generally exhibited decreasing concentration gradient offshore; while the highest levels of PAHs and Pb were observed near the southern Taiwan Strait, which may be induced by accumulation of different fluvial input. Historical records of pollutants followed closely with the economic development of China, with fast growth of Σ₁₅PAH and Pb occurring since the 1980s and 1990s, respectively. The phasing-out of leaded gasoline in China was captured with a sharp decrease of Pb after the mid-1990s. PAHs and Pb correlated well with TOC and clay content for core sediments, which was not observed for surface sediments. There was an up-core increase of high molecular PAH proportions. Coal and biomass burning were then qualitatively identified as the major sources of PAHs with DRs. Furthermore, shift toward less radiogenic signatures of Pb isotopic ratios after 1900 revealed the start and growing importance of industrial sources. Finally, a greater separation and quantification of various input was achieved by a three-factor PMF model, which made it clear that biomass burning, coal combustion, and vehicle emissions accounted for 40±20%, 41±13%, and 19±12% of PAHs through the core. Biomass and coal combustion acted as major sources before 2000, while contributions from vehicle emission soared thereafter. The integrated multi-methodologies here improved the source apportionment by reducing biases with a step-down and cross-validation perspective, which could be similarly applied to other aquatic systems.

Sources and mass inventory of sedimentary polycyclic aromatic hydrocarbons in the Gulf of Thailand: Implications for pathways and energy structure in SE Asia

Surface sediments obtained from a matrix of 92 sample sites in the Gulf of Thailand (GOT) were analyzed for a comprehensive study of the distribution, sources, and mass inventory of polycyclic aromatic hydrocarbons (PAHs) to assess their input pathways and impacts of the regional land-based energy structure on the deposition of PAHs on the adjacent continental margins. The concentration of 16 PAHs in the GOT ranged from 2.6 to 78.1ng/g (dry weight), and the mean concentration was 19.4±15.1ng/g. The spatial distribution pattern of 16 PAH was generally consistent with that of sediment grain size, suggesting the influence of regional hydrodynamic conditions. Correlation and principal component analysis of the PAHs indicated that direct land-based inputs were dominantly responsible for the occurrence of PAHs in the upper GOT and the low molecular weight (LMW) PAHs in the coastal region could be from petrogenic sources. A positive matrix factorization (PMF) model apportioned five contributors: petroleum residues (~44%), biomass burning (~13%), vehicular emissions (~11%), coal combustion (~6%), and air-water exchange (~25%). Gas absorption may be a significant external input pathway for the volatile PAHs in the open GOT, which further implies that atmospheric loading could be important for the sink of PAHs in the open sea of the Southeast Asia (SE Asia). The different PAH source patterns obtained and a significant disparity of PAH mass inventory in the sediments along the East and Southeast Asia continental margins can be ascribed mainly to different land-based PAH emission features under the varied regional energy structure in addition to the depositional environment and climatic conditions.

Economy and carbon dioxide emissions effects of energy structures in the world: Evidence based on SBM-DEA model

Nowadays, the increasing global warming phenomenon caused by large carbon dioxide (CO₂) emissions has a huge impact on the economic and social sustainable development in the world. And CO₂ emissions come mainly from the burning of fossil energy, such as oil, natural gas and coal. Therefore, a novel economy and CO₂ emissions evaluation model based on the slacks-based measure integrating the data envelopment analysis (SBM-DEA) is proposed to analyze and optimize energy structures of some countries and regions in the world. The consumption of oil, natural gas and coal are inputs of the proposed method. In addition, per capita gross domestic product (GDP) value is the desirable output and CO₂ emission is the undesirable output. Then the economy and CO₂ emissions evaluation model of some countries and regions in the world is built. The results show that the overall efficiency of developed countries and regions is higher than that of developing countries. Moreover, due to the optimal configuration of slack variables of inputs and the undesirable output, the efficiency values of some inefficient countries and regions can be improved greatly. Furthermore, whether in 2017 or 2018, the average efficiency values of Europe and Oceania are both relatively high, and these two years average efficiency values of Asia are all the lowest among the five continents.

Exploring the impact of determining factors behind CO2 emissions in China: A CGE appraisal

Along with the arrival of the post-Kyoto Protocol era, the Chinese government faces ever greater pressure to reduce greenhouse gases (GHGs). Hence, this paper aims to discuss the drivers of carbon dioxide (CO₂) emissions and their impact on society as a whole. First, we analyzed the background and overall situations of CO₂ emissions in China. Then, we reviewed previous studies to explore the determinants behind China's CO₂ emissions. It is widely acknowledged that energy efficiency, energy mix, and economy structure are three key factors contributing to CO₂ emissions. To explore the impacts of those three factors on the economy and CO₂ emissions, we established a computable general equilibrium (CGE) model. The following results were found: (1) The decline of a secondary industry can cause an emission reduction effect, but this is at the expense of the gross domestic product (GDP), whereas the development of a tertiary industry can boost the economy and help to reduce CO₂ emissions. (2) Cutting coal consumption can contribute significantly to emission reduction, which is accompanied by a great loss in the whole economy. (3) Although the energy efficiency improvement plays a positive role in promoting economic development, a backfire effect can weaken the effects of emission reduction and energy savings.

The local-neighborhood effect of green credit on green economy: a spatial econometric investigation

Green credit is one of the most important financial instruments to promote sustainable development. Taking the provincial panel dataset of China as the research sample, this paper investigates the spatial impacts of green credit on the green economy. The super slack-based measure (Sup-SBM) model with undesirable outputs is employed to calculate the level of green economy within China. On this basis, we establish spatial Durbin models to study the impact of green credit on green economy and its transmission mechanisms. The results show that green credit exhibits a local-neighborhood effect on green economy; that is, the green credit can not only improve the local green economy but also generate spatial spillover effect to promote the development of green economy in surrounding areas. The above conclusion still holds after the robustness test by replacing spatial weight matrices and alternative measurement for the explained variable. Furthermore, enhancing innovation efficiency and optimizing energy structure are important ways for green credit to promote green economy. The findings of this study not only provide a new perspective for understanding the economic consequences of green credit policy but also provide empirical evidence for the important role of green finance in achieving the win-win goals of economic growth and environmental protection.

Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network

Nowadays, the world has achieved tremendous economic development at the expense of the long-term habitability of the planet. With the rapid economic development, the global greenhouse effect caused by excessive carbon dioxide (CO₂) emissions is also accumulating, which generates the negative impact of global warming on nature and human beings. Meanwhile, economy and CO₂ emissions prediction methods based on traditional neural networks lead to gradient disappearance or gradient explosion, making the economy and CO₂ emissions prediction inaccurate. Therefore, this paper proposes a novel economy and CO₂ emissions prediction model based on a residual neural network (RESNET) to optimize and analyze energy structures of different countries or regions in the world. The skip links are used in the inner residual block of the RESNET to alleviate vanishing gradients due to increasing depth in deep neural networks. Consequently, the proposed RESNET can optimize this problem and protect the integrity of information by directly bypassing the input information to the output, which can increase the precision of the prediction model. The needs for natural gas, hydroelectricity, oil, coal, nuclear energy, and renewable energy in 24 different countries or regions from 2009 to 2020 are used as inputs, the CO₂ emissions and the gross domestic product (GDP) per capita are respectively used as the undesired output and the desired output of the RESNET to build an economy and CO₂ emissions prediction model. The experimental results show that the RESNET has higher correctness and functionality than the traditional convolutional neural network (CNN), the radial basis function (RBF), the extreme learning machine (ELM) and the back propagation (BP). Furthermore, the proposed model provides guidance and development plans for countries or regions with low energy efficiency, which can improve energy efficiency, economic development and reasonably control CO₂ emissions.

Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network

The combustion of fossil fuels produces a large amount of carbon dioxide (CO2), which leads to global warming in the world. How to rationally consume fossil energy and control CO2 emissions has become an unavoidable problem for human beings while vigorously developing economy. This paper proposes a novel economy and CO2 emissions prediction model using an improved Attention mechanism based long short term memory (LSTM) neural network (Attention-LSTM) to analyze and optimize the energy consumption structures in different countries or areas. The Attention mechanism can add the weight of different inputs in the previous information or related factors to realize the indirect correlation between output and related inputs of the LSTM. Therefore, the Attention-LSTM can allocate more computing resources to the parts with a higher relevance of correlation in the case of limited computing power. Through inputs with the consumption of oil, natural gas, coal, hydroelectricity and renewable energy, the desirable output with the per capita gross domestic product (GDP) and the undesirable output with CO2 emissions prediction model of different countries and areas is established based on the Attention-LSTM. The experimental results show that compared with the normal LSTM, the back propagation (BP), the radial basis function (RBF) and the extreme learning machine (ELM) neural networks, the Attention-LSTM is more accurate and practical. Meanwhile, the proposed model provides guidance for optimizing energy structures to develop economy and reasonably control CO2 emissions.

Analysis of the efficiency and structure of energy consumption in the industrial sector in the European Union countries between 1995 and 2019

The industrial sector is one of the most important sectors of the global economy, having a huge impact on the development of individual countries and regions. This sector covers a wide and diverse range of activities, which makes it of key importance for the economy of the European Union (EU) countries. As a result, the processes related to energy transformation and climate policy are increasingly connected with the sector in question. The need to improve the competitiveness of the economy and the implementation of climate and energy strategies means that this sector, like the entire EU economy, must rapidly enhance its energy efficiency and the structure of energy consumption. The following paper addresses this problem by presenting the results of a comprehensive study of the structure and volume of energy consumed by this sector in the period between 1995 and 2019. Based on this study, quantitative changes and the structure of energy consumed in this sector in the studied period were determined for the entire EU and its individual countries. The use of the Gini coefficient and the Lorenz curves allowed for the determination of the inequality of energy consumption in the industrial sector. The coefficients of variation and the dynamics of changes in energy consumption, both in total and from individual sources, for the EU countries were also determined. The aim of this part of the study was to indicate directions and the intensity of changes related to the structure and consumption of energy in this sector. In the next stage, groups of similar countries were created and compared in terms of the structure of energy consumed by the industrial sector in 1995 and 2019 (using the Kohonen's neural network). Relationships between the amount of energy consumed by the industrial sector in the entire EU and the basic economic and climate parameters of the economy were also delineated. The energy intensity of this sector and the dynamics of its changes in individual EU countries over the analyzed period were also specified. The results proved a great diversity of the EU countries and the improving energy efficiency and structure of energy consumed by the industrial sector. The research, together with its results, significantly broaden the knowledge of changes in the volume and structure of energy consumption in the industrial sector for the EU countries. The results make it possible to assess the actions of individual countries and the current state of implementation of EU climate and energy policy. They should also be used to develop future assumptions of this policy.

Do economic policy uncertainty and environment-related technologies help in limiting ecological footprint?

Economic policies related to energy and the environment are found uncertain in developing economies. Renewable energy sources are gradually increasing in energy structure (ES) with the adoption of environment-related technologies (ERT). However, least attention is paid to investigating the nexus of economic policy uncertainty (EPU), ERT, ES, and ecological footprint (EF). Therefore, this study is an effort to examine the EPU, ERT, ES, and interaction of EPU and ERT on EF for BRICS economies under the umbrella of the STIRPAT model. By using the data from 1992 to 2020, findings are estimated through "cross-sectional dependence (CD test); CIPS and CADF unit root test; Westerlund's co-integration; and CS-ARDL, AMG, and CCEMG." Findings unveiled the negative role of EPU on EF. Furthermore, the role of RE and ERT is positive and substantial in decreasing the environmental degradation in BRICS. Therefore, the BRICS economies are suggested to be consistent on economic policies to catch the positive impact of ERT. Findings are robust to the policy implications.

Can green credit policy promote green total factor productivity? Evidence from China

Green credit, a market-driven environmental policy instrument and an essential component of the green financial system, has piqued academic and policymakers' interest in whether it has successfully improved Chinese green total factor productivity (GTFP). Utilizing Chinese province panel data from 2006 to 2019, this study assesses GTFP using the slack-based model with the Global-Malmquist-Luenberger technique and investigates the influence of green credit on GTFP as well as its mechanism. The findings suggest green credit has a favorable influence on China's GTFP. Green credit can boost GTFP through three mechanisms: upgrading industrial structure, stimulating green innovation, and optimizing energy consumption structures. Furthermore, green credit improves GTFP in eastern regions but has little impact elsewhere; the promotion impact is more effective in financial developed regions and legal developed regions. As a result, the Chinese government should encourage regionally differentiated green credit policy implementation.

Impact of Economic Structure on the Environmental Kuznets Curve (EKC) hypothesis in India

This study aims to evaluate the impact of economic structure on the Environmental Kuznets Curve (EKC) in India. The present study deviates from the bulk of study in the literature with the incorporation of both aggregated and disaggregated measures of economic development on the environmental degradation function. For the empirical analysis, the study employed the Auto-Regressive Distributed Lag (ARDL) bounds testing approach of cointegration to analyse the long-run and short-run relationship during 1971-2014. Further, the direction of the causality is investigated through the Wald test approach. The results revealed that the conventional EKC hypothesis does not hold in India in both aggregated and disaggregated models since economic growth and its component have a U-shaped impact on the environmental quality in India. However, the effect of population on environmental quality is positive but not significant in the aggregated model. Whereas, in the disaggregated model, it is significantly affecting environmental quality. Hence, it is possible to infer that the population of the country increases, the demand for energy consumption increase tremendously, particularly consumption of fossil fuel like coal, oil, and natural gas, and is also evident from the energy structure coefficient from both models. This increase is due to the scarcity of renewable energy for meeting the needs of people. On the contrary, urbanization reduces environmental degradation, which may be due to improved living conditions in terms of efficient infrastructure and energy efficiency in the urban area leading to a negative relation between urbanization and environmental degradation.

Effects of energy structure differences on chemical compositions and respiratory health of PM2.5 during late autumn and winter in China

To understand the influence of the energy structure (including solid fuel and clean energy) on air pollution, two comprehensive measurement campaigns were conducted in Baoding and Shanghai in late autumn and winter during 2017-2018. The chemical compositions, driving factors, regional transport of pollutants, and potential respiratory disease (RD) health risks of PM_2.5 for Baoding and Shanghai were analyzed. The results showed that the concentration of PM_2.5 in Baoding (156.9 ± 139.8 μg m^-3) was 2.6 times of that in Shanghai (60.9 ± 45.9 μg m^-3). The most important contributor to PM_2.5 in Baoding was organic matter (OM), while inorganic aerosols accounted for major fractions of PM_2.5 in Shanghai. Positive matrix factorization (PMF) results indicated that coal combustion (CC; 39%) accounted for the most in Baoding, followed by secondary aerosols (21%), biomass burning (BB; 20%), industrial emissions (14%), dust (3%), and vehicle exhaust (2%). However, the average contribution in Shanghai followed the order: secondary aerosols (44%), vehicle exhaust (36%), dust (11%), marine aerosols (6%), and BB (3%). The evolution of source contributions at different pollution levels revealed that haze episodes in Baoding and Shanghai were triggered by CC and secondary formation, respectively; however, the air quality on clean days in Baoding and Shanghai was affected mostly by BB and vehicle emissions, respectively. Potential source contribution function (PSCF) results suggested that CC in Baoding was primarily from local emissions, while BB was primarily from local and regional transport. Vehicle exhaust and secondary aerosols in Shanghai were mainly from local emissions and regional transport. The number of RD deaths related to haze episodes in Baoding and Shanghai were 215 (95% CI: 109, 319) and 76 (95% CI: 11, 135), respectively. This research also emphasized the importance of further attention to the usage of coal in Baoding and vehicle emissions in Shanghai.

Synergistic effect of energy and industrial structures on carbon emissions in China

Energy structure and industrial structure are two crucial economic factors affecting carbon emissions. However, current research often examines them separately, neglecting the potential additional synergistic effect between them. Leveraging the coupling concept from physics, we objectively quantify these synergistic effect and investigate influencing factors on CO2 intensity from a novel perspective of the synergy by combining a coupling coordination model with econometric model of generalized method of moments (GMM) with a panel dataset from China spanning 2007 to 2019. Our estimates indicate that (1) synergy of energy and industrial structures significantly reduces carbon intensity, which is stable after a series of robust check. (2) the reduced effect of synergy can be enlarged by enhancing environmental regulation and green innovation. (3) the inhibiting effect of synergy is significant, mainly occurs in regions with abundant energy resource endowments. Correspondingly, we recommend several policy implications for China and other developing countries.

Decoupling effect and forecasting of economic growth and energy structure under the peak constraint of carbon emissions in China

The decoupling effect between economic growth and energy structure was quantitatively analyzed from 1999 to 2014 across China. The results showed it existed weak decoupling effects in most regions. Based on the analysis of the influence of energy structure on carbon intensity, using scenario simulation methods and Markov chain modeling, the carbon intensity was predicted for China in 2020. The impact of energy structure adjustment on the carbon intensity to meet China's carbon target by 18 possible scenarios are calculated. Furthermore, the peak value of carbon emissions was also calculated in 2030. The results showed that the carbon intensity predicted for China in 2020 can be achieved regardless of whether the energy structure was adjusted or not when energy saving and carbon reduction policies maintained with economic growth at 6-7%. Moreover, given fixed energy structure growth, for each 1% of economic growth, the carbon intensity will decrease by about 3.5%. Given fixed economic growth, the decrease of energy intensity will be greater if the control of energy consumption is stronger. The effect of energy structure adjustment on the decreasing of carbon intensity will be 4% higher under constraints than without constraints. On average, the contribution of energy structure adjustment to achieving the carbon intensity target was calculated as 4% higher than that with constraints. In addition, given relatively fixed economic growth at 6-7%, the peak value of carbon emission in 2030 was calculated as 13.209 billion tons with constraints and 14.38 billion tons without constraints.

Linkage of natural resources, economic policies, urbanization, and the environmental Kuznets curve

Natural resource rents (NRR) and economic policies are considerably studied to determine ecological footprints. Currently, due to global uncertainty, renewable energy adoption, and increasing urbanization, every economy is facing challenges to control its ecological footprints. The available literature on the said linkages in the emerging seven economies is inconclusive. Therefore, this study is designed to re-estimate the linkages of NRR, urbanization (URB), economic policy uncertainty (EPU), energy structure (ES), and EFP under the "Environment Kuznets Curve (EKC) hypothesis." Data from 1992 to 2020 is used for empirical evidence, along with robust econometric calculations. The EKC hypothesis does not apply to the E7 economies, according to the findings. The energy structure is assisting in limiting ecological footprints and hence aids in environmental cleanup. The role of NRR, EPU, and URB in limiting the EF, on the other hand, is not encouraging. To minimize environmental degradation, emerging economies should reconsider their economic policies, natural resource rents, and rapid urbanization.

Measures to achieve carbon neutrality: What is the role of energy structure, infrastructure, and financial inclusion

Infrastructure and energy structure play key roles in the adaptation of a sustainable environment. In order to achieve a desirable infrastructure and energy structure, financial inclusion is essential. Thus, the current study investigates the nexus of energy structure, infrastructure, financial inclusion, and carbon emissions in the countries of the Organization for Economic Co-operation and Development (OECD). In particular, the well-known nexus of growth and environment is employed to estimate the linkages using data between 2001 and 2020. The findings suggest the supportive role of infrastructure, energy structure, and financial inclusion in abating carbon emissions. The OECD economies should enhance their investment in infrastructure and energy structure. Moreover, in order to achieve a sustainable environment in the long-run, financial inclusion should also be expanded. The results are also robust to the short- and long-run policy implications. This study is conductive to the implementation of the United Nations (UN) Sustainable Development Goals.

Other's shoes also fit well: AI technologies contribute to China's blue skies as well as carbon reduction

Artificial intelligence (AI) technology represents a disruptive innovation that has garnered significant interest among researchers for its potential applications in ecological and environmental management. While many studies have investigated the impact of AI on carbon emissions, relatively few have delved into its relationship with air pollution. This study sets out to explore the causal mechanisms and constraints linking AI technologies and air pollution, using provincial panel data collected from 2007 to 2020 in China. Furthermore, this study examines the distinct pathways through which AI technology can ameliorate air pollution and reduce carbon emissions. The findings reveal the following key insights: (1) AI technologies have the capacity to significantly reduce air pollution, particularly in terms of PM2.5 and SO2 levels. (2) AI technologies contribute to enhanced air quality by facilitating adjustments in energy structures, improving energy efficiency, and strengthening digital infrastructure. Nonetheless, it is important to note that adjusting the energy structure remains the most practical approach for reducing carbon emissions. (3) The efficacy of AI in controlling air pollution is influenced by geographical location, economic development level, level of information technology development, resource dependence, and public attention. In conclusion, this study proposes novel policy recommendations to offer fresh perspectives to countries interested in leveraging AI for the advancement of ecological and environmental governance.

Green finance, energy structure, and environmental pollution: Evidence from a spatial econometric approach

The adjustment of green finance and energy structure is gradually becoming a new engine that reduces environmental pollution in China. In this paper, the energy structure is introduced in the process of discussing the impact of green finance on environmental pollution. We analyze the spatial correlation of green finance and study whether the adjustment of energy structure is affected by green finance and thus affects environmental pollution using a spatial econometric model. The results of empirical analysis show that green finance among provinces presents a significant spatial agglomeration, improving the green finance, and the energy structure can significantly reduce environmental pollution, and there are significant spatial spillover effects. There is inverted U-shaped relationship between energy structure and green finance in the national space, that is, after the green finance is raised to a certain extent, with the level of green finance once more, the energy structure will gradually improve, and then, green finance drives the reduction of environmental pollution by improving the energy structure. The results of the heterogeneity analysis show that compared with other regions, the improvement of the green finance in the eastern region has significantly improved the energy structure, and environmental pollution has also decreased every year.

Did carbon dioxide emission regulations inhibit investments? A provincial panel analysis of China

Investments, especially fixed asset investments, greatly affect carbon dioxide (CO₂) emissions. When investments are concentrated in regions with high CO₂ emissions and high fossil energy consumption, the CO₂ emission reduction targets in these areas are difficult to reach in the short term, and the cost of CO₂ emission abatement is high. The current CO₂ emission regulations focus on existing production activities and consumption behaviors. However, whether an investment, which may affect CO₂ emissions in the long term, is effectively inhibited by CO₂ emission regulations has not been investigated in previous studies. Using panel data from 30 provinces in China between 2003 and 2012, we tested whether the amount of provincial investment was constrained or promoted by the provincial CO₂ emission regulations by creating a panel model with provincial samples. The results revealed that CO₂ emission regulations did not inhibit the growth of an investment, but they stimulated investments to varying degrees in different provinces. A relatively positive result is that provinces with stronger CO₂ emission regulations exhibited a relatively small contribution to investment promotion, while provinces with weaker CO₂ reduction policies demonstrated a relatively large contribution to investment growth. We also found that investment was correlated with the growth rate of the gross domestic product (GDP) in the northeastern and western provinces. Finally, we proposed policy implications based on the utilization of policy tools from the perspectives of investment, energy structure, and local protectionism.

Synergy evaluation of China's economy-energy low-carbon transition and its improvement strategy for structure optimization

Low-carbon economic development and energy transition are interactively linked. The synergetic development of the two subsystems is important to achieve the "double carbon" goal of sustainable development. First, this study proposes a model to measure the current synergy level of China's economy-energy low-carbon transition. Second, an optimization model is developed to improve industry and energy synergy levels through structure optimization. The synergy degree (SD) level of China's economy-energy low-carbon transition increased from 0 to 0.98 between 2005 and 2017. Furthermore, 69.2% of the periods are in a state of asynergy (SD < 0.6). By implementing the industry and energy structure optimization (OPT) scenario, the synergy level by 2035 can be 27.8% higher than the business-as-usual (BAU) scenario. Moreover, light synergy (0.6 ≤ SD < 0.8) could be achieved by 2025, and high-quality synergy (0.9 ≤ SD ≤ 1) by 2033 in the OPT scenario. Conversely, the synergy level can only achieve light synergy until 2035 in the BAU scenario. Compared to energy structure optimization, the low carbonization of the economic structure plays a more significant role in improving the synergy level of the transaction. These findings can provide support for China's policy-making regarding economic and energy transition.

Climate change, ambient air pollution, and students' mental health

The impact of global climate change and air pollution on mental health has become a crucial public health issue. Increased public awareness of health, advancements in medical diagnosis and treatment, the way media outlets report environmental changes and the variation in social resources affect psychological responses and adaptation methods to climate change and air pollution. In the context of climate change, extreme weather events seriously disrupt people's living environments, and unstable educational environments lead to an increase in mental health issues for students. Air pollution affects students' mental health by increasing the incidence of diseases while decreasing contact with nature, leading to problems such as anxiety, depression, and decreased cognitive function. We call for joint efforts to reduce pollutant emissions at the source, improve energy structures, strengthen environmental monitoring and gover-nance, increase attention to the mental health issues of students, and help student groups build resilience; by establishing public policies, enhancing social support and adjusting lifestyles and habits, we can help students cope with the constantly changing environment and maintain a good level of mental health. Through these comprehensive measures, we can more effectively address the challenges of global climate change and air pollution and promote the achievement of the United Nations Sustainable Development Goals.

Central vertical regulation and urban environment-biased technological progress: evidence from China

Technological progress in favor of cleaner production is the key to achieving low-carbon development in China. The Ambient Air Quality Standard (AAQS) issued by the Ministry of Environmental Protection (MEP) in 2012 was an essential policy for the central government to implement vertical environmental regulation. Therefore, based on the city-level panel data, this paper examines the impact of the central vertical regulation on urban environment-biased technological progress using the difference-in-differences method. The results show that central vertical regulation can significantly promote urban environment-friendly technological progress. Heterogeneity analysis shows that the driving effect of the central vertical regulation on urban environment-friendly technological progress is more obvious in the eastern regions, non-resource-based cities, large cities, and high-grade cities. Moreover, the channel analysis shows that the central vertical regulation mainly boosts the urban environmental technology progress toward cleaner production by strengthening government environmental governance, raising public environmental concern, and improving energy structure. The findings provide policy implications for evaluating the effectiveness of macro-environmental policy and promoting green sustainable development.

Does place-based green policy improve air pollution? Evidence from China's National Eco-Industrial Demonstration Park Policy

Air pollution is one of the serious environmental problems facing the world. This paper systematically investigates the impact and transmission mechanism of the construction of national eco-industrial parks (NEDPs) on urban air pollution based on Chinese city-level panel data from 2003 to 2021 using a staggered difference-in-differences (staggered DID) model. It is found that the construction of NEDP significantly reduces urban air pollution, a conclusion supported by the negative weight diagnostic test and two types of robust DID estimators. Mechanism analyses indicate that NEDP construction reduces urban air pollution mainly by improving regional environmental regulation, promoting green technology innovation and improving energy structure. In addition, the mitigation effect of NEDP construction on urban air pollution is heterogeneous by policy intensity, city resource endowment, city size and administrative status. Further tests show that the institutional environment enhances the air pollution mitigation effect of NEDP construction and that the better the degree of marketization, property rights system, legal system and market development in the place where the policy is implemented, the more conducive it is to amplify the air pollution suppression effect brought about by NEDP construction. Developing economies should take complete account of the characteristics of different regions when implementing place-based green policies to achieve synergistic development of the environment and the economy.