Social network analysis of regional transport carbon emissions in China: Based on motif analysis and exponential random graph model.

Given the significant impact of transportation-related carbon emissions on air quality and climate change, understanding the regional dynamics of these emissions is crucial. Despite numerous studies on carbon emissions, there is a lack of comprehensive analysis of China's interprovincial transport carbon emission correlation network. Based on China's provincial data from 2007 to 2021, we analyzed the network's basic structural characteristics and categorized it into four significant plates to investigate their interactions. Subsequently, motif analysis is employed to examine the micro-correlation patterns within the network, and the Exponential random graph model (ERGM) is utilized to analyze the network's formation mechanism. Findings reveal that: (1) Provinces with high correlation intensity are predominantly concentrated in the eastern region, such as Shanghai and Beijing. Additionally, provinces in the eastern region assume a central role in the transport carbon emission correlation network, mainly receiving carbon emissions from other provinces. In contrast, the western region primarily emits carbon emissions to other provinces, continuously converging towards the center. (2) The network is segmented into net beneficiary plate, net overflow plate, bidirectional spillover plate, and broker plate, with distinct roles and influences across different years. (3) Bidirectional correlation motif structures emerge as primary influencers within the network, although specific structures impede interregional communication and collaborative emission reduction. (4) Internal network's structural variables, such as mutuality, cyclic triple, and geometrically weighted edgewise shared partner, along with influencing factors including government intervention, urbanization rate, openness, fiscal expenditure on transport, and province adjacency significantly impact the formation of the transport carbon emission correlation network. The above transportation network research provides a theoretical basis for the country to promote low-carbon transportation and improve air quality, and also has important guiding significance for the cross-regional collaborative emission reduction work of provinces.

Can green finance improve eco-efficiency? New Insights from China.

Achieving economic development and ecological protection simultaneously is an inevitable selection for sustainable development in today's world, so it is crucial to improve eco-efficiency (EE). According to Chinese panel data at the provincial level between 2010 and 2020, this research explores the nexus between green finance (GF) and EE. The results denote that GF can significantly improve EE, and the higher the level of EE, the stronger the effect of improvement. The upgrading of industrial structure, optimization of energy structure, enterprises' concern for environmental protection and the public's attention to the environment are all favorable factors that can enhance the promotion effect of GF on EE. Additionally, this facilitation can only be played under a good external environment and mature internal conditions. Our findings can provide new insights for improving EE by developing GF.

Prediction of carbon emissions in China's construction industry using an improved grey prediction model.

As a significant source of global energy consumption and greenhouse gas emissions, the construction industry garners widespread attention due to its high carbon emissions. Anticipating its development trends is crucial for energy conservation and emission reduction. In this paper, we utilize the carbon emission data from China's national and provincial construction sectors from 2012 to 2021, employ the grey prediction model optimized by the particle swarm optimization algorithm, coupled with a metabolic algorithm, to forecast the carbon emissions of the construction industry across China and its provinces. The results demonstrate that: (1) The dynamic grey prediction model combined with the metabolism algorithm has a better prediction effect than the classical model, and the relative error is reduced from 5.103 % to 0.874 %. (2) The carbon emissions of China's construction industry will continue to rise in the next decade, but the growth rate will decrease, and the proportion of indirect carbon emissions continues to increase. (3) There is a marked regional disparity in carbon emissions, with the eastern region exhibiting higher emission levels yet slower growth. In contrast, the western region has lower emission levels but experiences faster growth. These studies provide valuable insights for both the existing approaches to energy conservation and emission reduction, as well as for future policy improvements.

A carbon neutral supply chain management by considering emission-risk minimization and green purchasing through optimal decision-making.

This study addresses critical gaps in supply chain management (SCM) by integrating emission-risk minimization (ERM), green purchasing (GP), and profit maximization (PM). The research focuses on the optimal behaviors of manufacturers, agents, and retailers within the SCM framework to achieve carbon neutrality and reduce carbon dioxide emissions (CO2e). This study considers Guangdong province, China, a region facing challenges in optimizing energy systems and meeting CO2e reduction targets. Simulation-based optimization techniques within mathematical models are employed. A design of experiment (DOE) method was used to explore the dynamics of key variables in the SCM environment. Results reveal optimal behaviors for manufacturers, agents, and retailers, demonstrating the ideal values for profit and pricing decisions. Manufacturers optimize production quantity, achieving CO2e reduction and PM through ERM. Agents exhibit a strong commitment to GP practices, enhancing PM and carbon-neutral goals. Retailers get more PM than manufacturers and agents, contributing to a clean environment. Interestingly, retailers make contributions to the clean environment without considering ERM and GP in SCM. The study contributes novel insights by addressing the identified gap in SCM research, emphasizing the joint consideration of ERM, GP, and PM. This research assists manufacturers, agents, and retailers in terms of PM for economic objectives. It cleans the environment through carbon-neutral SCM in society.

Construction of China's food security evaluation index system and spatiotemporal evolution.

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

Efficiency evaluation of China's transportation system considering carbon emissions: Evidence from big data analytics methods.

China's transportation sector is a vital link between production and consumption, but it also has issues with low efficiency, high carbon emissions, and technological bottlenecks. To improve efficiency and provide actionable recommendations and strategies, this study first constructs a comprehensive evaluation index system to gauge the transportation sector's inputs using panel data from different Chinese provinces from 2007 to 2021. Within the assessment system, the principal component analysis (PCA) method is used to reduce the dimension of the indexes, thereby yielding a set of adjusted inputs. Subsequently, the transportation system efficiency (TSE) is evaluated using the super-efficiency SBM-DEA model, which includes unexpected outputs such as carbon emissions, and three-stage DEA modifies the efficiency. Then, we calculate the Malmquist-Luenberger index (TML) and its components: technological change (TTC) and technological efficiency change (TEC). Lastly, the influential factors impacting TSE are analyzed via a truncated regression Tobit model. The following are the conclusions: (1) The transportation industry in China exhibits inefficiency, and the average TSE in Stage I and III is 0.91 and 0.93, respectively. TSE is underestimated due to the influence of external environmental factors and inefficiencies in management in Stage I. (2) TSE in the eastern area also produces significant carbon emissions that surpass the national average. At the same time, other regions face efficiency limitations due to geographical constraints and management obstacles. (3) Insufficient technical capacity is a major cause of inefficiency in the transport sector and is prevalent in the northeast, west, and central regions. (4) Population growth and income per capita advancements foster transportation industry development, while increased GDP, fiscal revenues, and traffic accidents contribute to declining efficiency. The study above findings serve as a foundation for regional and national management initiatives and policies to enhance transportation effectiveness.

China's sustainable carbon neutral supply chain management: a reference to global emission reduction.

Carbon emissions are a critical concern in the realm of energy consumption, with many nations committed to curbing them and striving for carbon neutrality. This research addresses a specific gap in the literature by focusing on carbon emission reduction in the industrial sector through the application of supply chain management (SCM) principles under a monopolistic competition context and by utilizing carbon neutral indicators. Specifically, we develop a mathematical model integrated into an agent-based simulation framework and employ experimental design methods to determine the most optimal decision-making processes for incorporating supply chain carbon neutral indicators. Our findings highlight the effectiveness of two specific carbon neutral indicators within SCM: green-credit policies and the promotion of green products. These measures contribute significantly toward reducing global carbon emissions. This paper offers insights for government decision-makers regarding ideal implementation levels and strategies for green credit policies and the promotion of green products within supply chain environments. It also provides guidance to manufacturers on how to achieve profit maximization while progressing towards carbon neutrality.

What impacts do green bonds have on carbon emissions and how? A dynamic spatial perspective in China.

Green bonds are adopted to exclusively finance or refinance green projects and thus is an important policy instrument for sustainable development. The development of these eco-friendly projects benefits the reduction of carbon emission. What impacts do green bonds have on carbon emission intensity and how? This issue needs to be revealed, including the dynamic spatial interactive rules and regional heterogeneity. This is especially true for China, with its vast territory and a short history of green bonds. Different with existing literature, this paper collects the data of the amount of green bond issued in each province in China rather than the policy dummy variable of green bond. The spatio-temporal interactions of both the impact of green bonds and the related mechanisms are studied. A dynamic spatial Durbin model (DSDM) combined with a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model reveals the negative impacts of green bonds on carbon emissions and the spatio-temporal interactions. On this basis, the mediation model is introduced to clarify the three impact mechanisms of green bonds and find the predominant role of technology mechanism. In addition, different characteristics in spatial interactive rules and impact mechanisms of green bonds are found in various regions of China. Finally, the study proposes some policy recommendations on how to effectively reduce carbon emissions with green bonds.

The role of GICT and environmental regulation in affecting ecological footprint.

Research studies have recently diverted attention towards the determinant of ecological footprints, but related issues have not provided consistent results. Based on the IPAT model (environmental impact (I) is decomposed into three elements: population (P), affluence (A, economic growth), and technology level (T)), this paper empirically explores the validity of the green information and communication technology (GICT)-induced environmental Kuznets curve (EKC) hypothesis. The research applies a quantile regression (QR) that tests over 95 countries' panel data for the period 2000-2017 by using six types of ecological footprint (EF) as environmental degradation indicators and environmental regulations (ERs) as interaction variables. We confirm the vital role that GICT plays in lessening cropland, forest area, and grazing land, while increasing its impact on built-up land. Additionally, the findings partially support the existence of an inverted U-shaped GICT-induced environmental EKC hypothesis for a decreasing impact on cropland, forest area, and grazing land via consideration of non-market-based ER as the interaction term. GICT does not notably reduce carbon-absorption land usage; however, improvements of GICT and non-market-based ER in those nations have been accompanied by lower environmental degradation.

The asymmetric effect of agriculturalization toward climate neutrality targets.

Agricultural activities immensely contribute to greenhouse gas emissions (GHGs). This study investigates the heterogeneous effect of agricultural production (AGRIP) on three major GHGs emissions, i.e., carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) under the Environmental Kuznets Curve (EKC) framework using a balanced panel data of 90 countries from the period 1991 to 2019. Second-generation panel unit root and cointegration tests are conducted to account for cross-sectional dependence. The findings suggest that there is a long run equilibrium among target variables. Evidence from panel quantile regression suggests that AGRIP significantly reduced CO2 emissions, and the effect is stronger in lower quantiles (least carbon emitters). On the other hand, AGRIP increases N2O and CH4 emissions in all quantiles. However, AGRIP is homogeneously distributed across N2O quantiles while the effect is stronger in higher quantiles (high methane emitters) in the case of CH4 model. Concerning agricultural trade, exports impede CO2 emissions but increase N2O and methane emissions. Agricultural imports are positively associated with all GHGs emissions. The effect of agricultural trade is largely heterogeneous and varies across different quantile levels of GHGs emissions. The EKC is fully valid for CO2 and N2O but not for the methane emissions model. Based on the results, it is suggested that high GHGs emitter should shift their agricultural activities from traditional to sustainable approaches along with green trade policies to achieve climate neutrality targets.

What does export diversification do for energy demand? Evidence from the Global North.

This paper contributes to the trade-energy literature by examining the effect of export product diversification on the energy demand of 30 countries located in the Global North over the period 1980-2014. A nonparametric time-varying coefficient panel data model with fixed effects is employed for its ability to produce robust outcomes in the presence of parameter instabilities, nonstationarity, regime shifts, and time variations. As a second step, the paper also utilizes the nonparametric fixed effects extension of Driscoll and Kraay, which is robust to spatial/cross-sectional dependence, as well as to autocorrelation and heteroscedasticity. The results indicate that export diversification lowers overall energy demand in the Global North, and the size of the impact has been on a gradual increase over the years. The paper concludes that if a conscious effort is made to ensure that product diversification is towards energy-efficient goods, export product diversification can serve as a useful strategy for managing energy consumption and mitigating its negative environmental effects.

The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach.

The study investigates the asymmetric effect of temperature, exchange rate, metals (rare metals and electrical conductors), and investor sentiments on solar stock price performance in China. The novel econometric techniques, i.e., QARDL (quantile autoregressive distributive lag) approach and Granger causality-in-quantiles to analyze the results. In both short- and long-run estimations, the findings suggest that rare metals (cadmium, germanium, indium, and selenium) and electrical conductors (silver, aluminum, and copper) have significant and positive linkage with solar energy stocks at different quantiles based on bullish, bearish, and normal market conditions. On the other hand, negative effects are found for temperature, RMB exchange rate, and investor sentiments in both the short- and long-run. In the short run, the effect of exchange rate varies across different quantiles but it confines to only lower quantiles (bearish market condition) in the longer run. Solar stocks are more prone to investor sentiments under higher quantiles (bullish market conditions). Lastly, we find that temperature is not merely a behavioral anomaly for the solar energy market as it spreads across middle quantiles (normal market conditions) in the longer run. The findings of Granger causality in quantiles further confirm the results of QARDL.

The impact of air quality on international tourism arrivals: a global panel data analysis.

Using data from World Development Indicators (WDI), this research constructs panel data of 99 countries from 1996 to 2018 and employs a spatial econometric model to analyze the impact of air quality on international tourism arrivals. Evidence shows that Moran's I values are significantly positive, indicating a strong positive spatial dependence in each country and that poor air quality does have a negative impact on the number of tourist arrivals. The results of grouped data illustrate that middle-income countries, low-income countries, high concentrations of PM2.5, and countries with less numbers of tourists have negative effects on tourist arrivals in neighboring countries. The contrary groups, however, have positive effects on tourist arrivals - that is, the influence of air quality on the number of tourist arrivals exhibits heterogeneity. In addition, tests of the interaction term show that countries with higher R&D intensity have better air quality and thus attract more tourists. Therefore, countries with poor air quality should improve the environment through international cooperation and undertake technology transfer, thus ultimately increasing the number of tourists.

Optimal behavior of environmental regulations to reduce carbon emissions: A simulation-based dual green gaming model.

As carbon emissions are one of the major problems of an emission-generating firm (EGF), there is a dire need to reduce them, and so this study fills this gap in the literature by considering the optimal behavior of environmental policies. This research develops policy measures under a duopoly game model such as carbon emission tax (CET) and subsidy on green investment (SGI). Additionally, we formulate a simulation model to measure the optimal behavior of CET, SGI, and production quantity to maximize profit and carbon emission reduction. The findings indicate that when green investment decreases, the EGFs are better able to pay CET and the government is also capable of providing SGI. Moreover, a lower unit production cost reduces more carbon emissions versus a higher unit production cost. In this way, the government receives revenue due to CET implementation and an EGF obtains revenue due to SGI. Both parties gain benefits at the same time and play important roles in cutting carbon emissions to make the environment clean. This study helps governments in finding their own optimal CET and SGI. An optimal SGI assists decision-makers at reducing carbon emissions and targeting profit maximization.

Environmental regulation, innovation capability, and green total factor productivity: New evidence from China.

Given China' rapid transformation, its economy is currently experiencing a change from a rugged development style to a sustainable development style with green development being critical to this transformation. Increasing green total factor productivity (GTFP) is now considered one of the significant signs of economic growth. With increasingly stricter environmental laws and regulations and a broad application of innovation capability (ICY) in modern life, this research investigates the impact mechanisms of both environmental regulations (ER) and ICY on GTFP, offering robust empirical results from panel data of 30 provinces in China covering the period 2006-2017. The results indicate that both ICY and ER effectively promote GTFP, but compared to ER, ICY has a heterogeneous effect on GTFP. Moreover, ICY plays a mediating role in ER and GTFP, while ER promotes GTFP through ICY. Accordingly, the paper puts forward some suggestions, such as to optimize and improve the ER policy, enact an innovation-driven development strategy, widely use ICY technology, and strengthen monitoring and supervision.

The criticality of tourism development, economic complexity, and country security on ecological footprint.

What kinds of countries are likely to be prosperous and have a sustainable environment at the same time? How might countries reorient their policy setting to be more capable of suppressing environmental degradation? To explore these questions, this research examines data from 99 countries for 2006-2017, takes the six major forms of ecological footprint (EF) as indicators of environmental quality, and probes the environmental Kuznets curve (EKC) hypothesis via quantile regression approach. We find that tourism development leads to greater environmental degradation, with tourism development particularly corresponding to more usage of carbon absorption land and cropland. The lower the country security is, the better is the environmental quality. Economic complexity also worsens environmental quality. However, country security weakens the negative influence of tourism development and economic complexity on environmental quality, specifying that better country security stalls the negative impact of tourism and economic complexity on environmental quality. Results mostly support the tourism- and country security-induced EKC hypotheses in fishing footprint, whereas economic complexity-induced EKC is generally validated in cropland footprint. Finally, we present that tourism arrivals, economic complexity, and country security have varying impacts across diverse ecological footprint quantiles.

CO2 emissions, income inequality, and country risk: some international evidence.

This research explores the effects of income inequality and country risk on CO2 emissions and examines whether the effects change across countries with different development stages or income levels. A new panel quantile regression approach is used to conduct a comprehensive analysis of the impacts of affecting factors on CO2 emissions at various quantiles, while addressing econometric challenges such as endogeneity and heterogeneity. From a global perspective, we can conclude that the marginal impact of inequality on emissions drops constantly with decreasing country risk at 10th to 50th quantiles, which even performs negative, whereas at the other quantiles, the marginal impact of inequality always remains negative. When we focus on the different income groups, the nexus of inequality emissions is negative first and then positive with decrease of country risk in low-income countries but shows no significant in low-middle- and upper-middle-income countries. Additionally, we validate the detrimental impact of income inequality in upper-income countries. Besides, country risk adversely moderates the nexus of inequality and emissions in low- and upper-income countries. Empirical results confirm that the nexus of inequality emissions lies in country risk, income level, and existing emission degree. These findings provide some important recommendations for policy-makers.

Gasoline demand elasticities in the world's energy gluttons: a time-varying coefficient approach.

In this paper, we estimate the price and income elasticities for gasoline demand in selected energy gluttons-China, India, USA, Russia, and Japan. Specifically, we employ a time-varying parameter approach which adequately deals with potential parameter instabilities and nonlinearities and effectively captures price and income elasticity variations over time, with each time period having its own set of coefficients. Our empirical findings reveal the following: gasoline consumption is price-inelastic and income-inelastic, there are movements in both the price and income elasticities, and the movements generally correspond with business cycle patterns of each of the countries; overall, sensitivity to price and income changes increase during periods of economic crises. Constant elasticity models overestimate price and income elasticities, and income is predominantly more elastic than price. Our conclusion is that policy mechanisms that are price-based such as gasoline taxes are likely to be unsuccessful in achieving consumption-cum-pollution reduction objectives in the energy gluttons. Such policies may, however, be effective if they ensure that gasoline prices rise at a greater rate than income. Such policies may also be useful for revenue-raising purposes.

Digitalization and environment: how does ICT affect enterprise environmental performance?

Despite the increasing use of digital technology in industrial production, how industrial digitalization affects the environmental performance of production activities remains unclear. This research contributes to the literature on the relationship between industrial digitalization and enterprise environmental performance by employing a large sample of Chinese manufacturing enterprises. Results indicate that the environmental performance of manufacturing enterprises has been significantly improved in the process of industrial digital transformation. Structural and technology effects are the transmission channels; additionally, structural effect is the main contributor to the positive environmental effects of information and communications technology (ICT) penetration. Industrial digitalization reduces the production scale of heavy-polluting enterprises and improves product innovation and green total factor productivity, but it has an insignificant effect on total factor productivity. Moreover, industrial digitalization improves enterprise environmental performance by introducing front-end cleaner production technologies, rather than by increasing pipe-end pollutant treatment facilities.

The impact of outward foreign direct investment on green innovation: the threshold effect of environmental regulation.

Outward foreign direct investment (OFDI) in an open economy has gradually become an important source of green innovation (GI). With the rapid development of China's OFDI, this research studies the impact of OFDI on the country's GI, employing panel data of 30 provinces from 2006 to 2017. We first use the Super-SBM model to measure GI performance and then test the impact of OFDI on GI with the system GMM model. Evidence finds that the negative impact of OFDI on GI is not significant on the whole, but the results of regional regression show that impact of OFDI on GI exhibits obvious regional differences. We then utilize the dynamic threshold panel model to determine the non-linear relationship between OFDI and GI through the perspective of environmental regulation in order to avoid the bias caused by ignoring the impact of institutional factors and time dynamic change. After dividing environmental regulations into command control environmental regulation and market incentive environmental regulation, the research results show that the double threshold effects of both environmental regulations are significant. Command control environmental regulation does not play a role in promoting the effect of OFDI on GI. When the intensity of market incentive environmental regulation is low, OFDI negatively affects GI. Moreover, only when the market incentive regulation shows high intensity can OFDI significantly promote GI. With the continuous growth of China's OFDI, it is therefore necessary to determine the appropriate environmental regulation to improve the reverse spillover effect of OFDI enterprises on the country's GI.