Is renewable energy development endangering power supply reliability?

The development of renewable energy is indispensable to promoting the low-carbon transition of power systems. Nevertheless, it also brings uncertainty to the reliability of power systems. Herein, the panel model and panel threshold model are established based on the provincial data in China from 2012 to 2020. The results reveal that the negative effect of renewable energy development (RED) on power supply reliability (PSR) is gradually lessening. If the development of renewable energy is a rational way, power supply reliability can be improved. Additionally, energy-exporting regions bear more risks of RED than energy-importing regions. If the coal prices are stable and natural disasters are manageable, the RED can enhance the PSR. However, if they are not stable or controllable, a high proportion of renewable energy in the power system could cause even more severe problems with PSR. Based on these critical results, some suggestions are made to promote the formation of a new power system.

A dynamic panel threshold model analysis on heterogeneous environmental regulation, R&D investment, and enterprise green total factor productivity

Environmental regulations are important means to influence manufacturing enterprise green development. However, there are two completely different conclusions both in theoretical and in empirical research, namely the "Follow Cost" theory and the "Porter Hypothesis". The nonlinear mechanism needs to be considered. Therefore, this study aims to explain the threshold impact of heterogeneous environmental regulations on enterprise green total factor productivity. Environmental regulations are divided into different sub-categories, then based on the panel data of 1220 Chinese manufacturing listed companies from 2011 to 2020, this paper uses threshold regression model to examine the impact of heterogeneous environmental regulations on Chinese manufacturing enterprise Green Total Factor Productivity. The empirical results show that: (1) Command-controlled, market-incentive and voluntary-agreement environmental regulation all have a significant nonlinear impact on enterprise Green Total Factor Productivity. (2) Enterprise R&D investment plays a threshold role in the impact. (3) There are industry and equity type differences in the impact process. This study focuses on the micro level of enterprises and tests the threshold mechanism, which make some theoretical complement to previous researches. The research results are not only beneficial for the government to propose appropriate environmental regulatory policies, but also for enterprises to achieve green growth through heterogeneous R&D investment.

How do energy consumption, globalization, and income inequality affect environmental quality across growth regimes?

This paper investigates the impacts of renewable and nonrenewable energy consumption, income inequality, and globalization on the ecological footprints of 49 countries for the period of 1995-2018. Panel cointegration test reveals a long-run relationship between the variables. Long-run parameter estimates derived from AMG and CCEMG, increasing income and nonrenewable energy consumption, have a significant positive impact on the ecological footprint, while countries that consume more renewable energy have seen an improvement in the quality of the environment. Conversely, neither income inequality nor globalization has a significant effect on national EFs. Evidence from the estimation of the panel threshold error correction model, where GDP growth is used as the transition variable, indicates a significant threshold effect, which supports a nonlinear relationship among the variables by identifying two distinct growth regimes: lower and upper. For the estimation sample, the positive and significant parameter estimates for economic growth in both growth regimes do not support the EKC hypothesis. The results indicate that renewable and nonrenewable energy consumption has a larger impact on the EF in the upper than lower growth regime. The threshold estimates are in line with the linear long-run estimates that do not indicate that income inequality has a significant impact on ecological footprint. However, globalization appears to negatively affect environmental quality in the lower growth regime.

The impact of agricultural productive service on agricultural carbon efficiency-From urbanization development heterogeneity

Agricultural productive service plays an important role in China's modernization of agricultural production, and its development is closely related to urbanization. However, its effect on agricultural carbon efficiency has seldom been discussed at different stages of urbanization level. Thus, the paper investigated the relationship between agricultural productive service and agricultural carbon efficiency considering the change in urbanization level, using panel data of China's 31 provinces from 2010 to 2020. We find that agricultural productive service can promote agricultural carbon efficiency, and its promotion effect of agricultural productive service will become more powerful with the increase of urbanization level. In addition, its promotion effect is relatively powerful in China's eastern provinces, while relatively weak in central and western provinces characterized by low urbanization levels. According to the paper's findings, we propose that we should strengthen the development of agricultural productive service in breadbasket provinces and focus on the coordinated development between agricultural productive service and urbanization.

Impact of income inequality on carbon emissions: a matter of corruption governance

Corruption is often linked with income inequality and its impact on carbon emissions. This study investigates the moderating effect of corruption governance on the relationship between income inequality and carbon emissions. Panel data for 62 countries from 2012 to 2020 were used. We employed a threshold panel regression approach, considering income inequality as the explanatory variable and carbon dioxide emissions as the dependent variable, with corruption governance as the threshold variable. Our findings suggest that enhancing the level of corruption governance can mitigate the CO2 emissions driven by income inequality. Specifically, we found a shift in the impact on CO2 emissions when corruption governance crosses a certain threshold. This study provides insights into how improving corruption governance can help in managing the environmental effects of income inequality.

Does artificial intelligence (AI) reduce ecological footprint? The role of globalization

This article explores the impact of artificial intelligence (AI) on global ecological footprints, which has important implications for global sustainability in the digital age. Using the comprehensive evaluation index of AI constructed by the entropy method and the dataset at the global national level, we find that from 2010 to 2019, the overall level of global AI shows an upward trend, in which the growth rate of AI in developed countries is more pronounced and exhibits a stable growth trend, while the growth rate of AI in developing countries displays a trend of instability. The research results show that AI has a significant inhibitory effect on ecological footprints. This conclusion holds even after endogeneity and robustness tests. In addition, under the effect of globalization, the impact of AI on ecological footprints shows nonlinear characteristics. As globalization deepens, the marginal effect of AI in reducing the ecological footprint shows an increasing trend. These findings emphasize the important role of AI in environmental governance and provide a new and comprehensive perspective for policymakers. Therefore, the government should continue to support the research and application of AI, promote the cross-industry integration of AI, and play a positive role in the process of globalization to promote global sustainable development.

Threshold effects of population aging on carbon emissions: From the perspective of industrial structure and residential consumption

Population aging and climate change caused by anthropogenic greenhouse gas emissions are two of the major challenges facing contemporary humanity. Based on panel data for 63 countries from 2000 to 2020, this paper empirically identifies and explores the threshold effects of population aging on carbon emissions, and tests in a causal inference framework the mediating effect mechanism of aging on carbon emissions through two pathways: industrial structure and consumption. Results show that generally when the percentage of the elderly population is higher than 14.5 %, carbon emissions related to industrial structure and residential consumption are significantly reduced although the threshold effects differ across countries. Particularly for lower-middle-income countries, the direction of the threshold effect is uncertain, which indicates the less importance of population aging for carbon emissions in these countries.

Examining the role of ICT, transportation energy consumption, and urbanization in CO2 emissions in Asia: a threshold analysis

ICT is viewed in earlier research as a double-edged sword that may either help or hurt the environment. Asian nations' ICT penetration has significantly expanded in recent years, and they are eager to bring about a digital revolution by building up their ICT infrastructure while consuming less energy for transportation and urban growth. Therefore, the purpose of this article is to investigate how ICT might reduce CO₂ emissions through the use of transport energy and urban development. Empirical and theoretical debates have been remaining ambiguous and contentious topic of whether energy consumed by the transport sector and urbanization causes CO₂ emanation in Asia, and what role ICT played in determining the level of CO₂ remains unanswered. This study adds to the ongoing discussion for sustainable transportation in ten Asian nations for 30 years that concentrate on the relationship between the energy consumption of transport, urbanization, ICT, and carbon emanation (1990-2020) and checked the validity of EKC. The STIRPAT and panel threshold models having two regimes are used to explore the stochastic impacts of the dependent and explanatory variables. We have divided explanatory into two categories, that is, the threshold variable ICT and the regime-dependent variables urbanization and transport energy consumption. Our results confirm that the EKC hypothesis holds in these Asian economies. Thus, our findings indicate that the environmental quality improves in terms of reduction in CO₂ emissions when ICT passes the threshold level due to the technological advancement in ICT dominating the scale effect induced by ICT. Furthermore, the possible policy recommendations are discussed according to the findings.

How does agricultural industrial structure upgrading affect agricultural carbon emissions? Threshold effects analysis for China

China continues to emphasize the importance of reducing agricultural carbon emissions and promoting the upgrading of its agricultural industry structure. However, the relationship between the two is rarely examined. This study aims to investigate the impact of upgrading the agricultural industry structure on agricultural carbon emissions. A two-stage instrumental technique and a threshold regression model are used in this study's analysis. The results indicate that agricultural industrial structure upgrading reduces agricultural carbon emissions by a statistically significant amount above the threshold of 0.378. The examination of the underlying mechanism shows agricultural energy efficiency and off-farm work as mediators of the nonlinear relationship between agricultural industrial structure upgrading and agricultural carbon emissions. Only when the agricultural energy efficiency and off-farm work thresholds are surpassed can improving the structure of the agricultural industry minimize agricultural carbon emissions. Analysis of heterogeneity indicates that the threshold for lowering agricultural carbon emissions is greater in northern China, but the potential for reduction is greater.

Green credit and industrial green total factor productivity: The impact mechanism and threshold effect tests

Green credit is an important financial policy tool to solve environmental pollution problems. Improving industrial green total factor productivity (IGTFP) is the key to promote industrial green development. Our study adopts provincial data from 2005 to 2020 to investigate the influence of green credit (GC) on IGTFP. We find that GC significantly improves IGTFP on the whole, industrial structure upgrading and green innovation are the two key impact paths. Threshold model tests show that with the increase of GC, human capital and R&D intensity, the promoting effects of GC on IGTFP are significantly enhanced. Heterogeneity tests indicate that the promoting effect of GC on IGTFP was further enhanced after 2016, GC significantly promotes IGTFP in eastern China, but it is not obvious in central and western China. Besides, the promoting effect of GC on IGTFP is significantly enhanced with the increase of IGTFP. Our research shows that the government should further optimize the green credit system and play the role of green credit in promoting green innovation and industrial structure upgrading.

Can China's carbon trading policy improve the profitability of polluting firms: a retest of Porter's hypothesis

The future trends and development trajectory of China's carbon emissions trading scheme (ETS), one of the key policy instruments for curbing peak carbon emissions and achieving carbon neutrality, have drawn a lot of interest. However, the Porter hypothesis (PH) and its validity boundary have not been explored sufficiently. We use micro-firm data from 2010 to 2019 to investigate whether the triple-difference (DDD) method could reveal the weak PH on the policy viewing ETS as a quasi-natural experiment in this work. Meanwhile, we use the panel threshold model and the moderated mediation effect model to assess the scientific border of the PH on the ETS. The findings show that by verifying the weak PH, the ETS may greatly enhance investment and foster the inventiveness of heavy-polluting industries (HPE). In contrast, the strong PH on the ETS has unstable validity and has non-linear characteristics. In particular, the ETS shows a U-shaped link between innovation and profitability by first decreasing and then increasing HPE's profitability through R&D. The cost of R&D and compliance costs being combined negatively impacts HPE's profitability. Further analysis shows that ETS will have different effects on the profitability of HPE due to R&D level and the threshold change of the compliance cost. This paper will offer some insightful points of view for the implementation of carbon market mechanisms in developing nations like China.

Do human capital and governance thresholds matter for the environmental impact of FDI? The evidence from MENA countries

This paper studies whether foreign direct investment (FDI)-CO2 emissions relationship may change depending on the data-driven estimated threshold levels for the country characteristics (CC) including human capital and governance in a sample of 13 Middle East and North Africa (MENA) economies during the 1996-2019 period. Our results strongly suggest that endogenously estimated CC thresholds matter for the impact of FDI on CO2 emissions. The pollution haven hypothesis, which maintains that FDI is associated with higher levels of pollution, appears to be valid for economies with weak CC. In addition to this, the pollution halo argument suggesting FDI lowers the emissions appears to be hold in countries with strong CC. The results in this study may indicate that policies aiming to improve human capital and governance may be expected not only to increase the economic benefits of FDI in terms of growth but also mitigate the negative environmental impacts of FDI in the MENA region.

Can outward foreign direct investment improve China's green economic efficiency?

Under the constraints of energy and environment, improving green economic efficiency (GEE) has become the key path to promote the sustainable economic development. Among the driving factors of GEE, the role of outward foreign direct investment (OFDI) is worth exploring. In this paper, we adopt the inter-provincial panel data of China from 2011 to 2019 and System Generalized Method of Moments (SYS-GMM) to explore the influence of OFDI on GEE. We find that OFDI significantly improves China's GEE, and reverse technology spillover through direct investment in developed countries is an important way for OFDI to promote GEE. Regional heterogeneity test shows that OFDI significantly promotes GEE in eastern China; however, the promotion effect is not significant in midwestern China. Besides, the promoting effect of OFDI on GEE has been further improved after 2016. We further adopt panel threshold model and find that when the financial development (FD) and human capital (HUM) exceeds 2.0954 and 0.0290, respectively, the promoting effects of OFDI on GEE are greatly enhanced. We suppose that the above conclusions can provide guidance for policymakers to optimize OFDI and improve GEE.

Have environmental regulations restrained FDI in China? New evidence from a panel threshold model

This paper aims to investigate the effect of environmental regulations on inward foreign direct investment in China. For this purpose, a panel threshold model was constructed to assess the threshold effects of environmental regulations on the influx of foreign direct investments (FDI) . The findings indicate that, under the influence of human capital, the impact of environmental regulations on FDI in China was characterized by a V-shaped curve, indicating an initial inhibitory effect followed by a subsequent increase. A plausible explanation is that specific pollution-generating FDI must withdraw from China because of stringent environmental regulations before human capital reaches a certain threshold level. Meanwhile, impaired by the adverse selection effect, some cleaner-production FDI cannot easily enter China. As a result, environmental regulations in this stage have an inhibitory effect on FDI in China. However, part of the pollution-generating FDI is converted into cleaner production after the human capital level reaches the threshold limit. Further, due to the positive selection effect, additional cleaner-production FDI can also enter China from different destinations. At this stage, environmental regulations boost overall FDI entering China.

How digitalization and financial development impact eco-efficiency? Evidence from China

The rapid growth of industrial digitalization and financial support are the main driving forces for the green transformation of China's economy. Aiming to explore how digitalization and financial development impact ecological efficiency (eco-efficiency), this study proposes a unified research framework by integrating multiple technologies using the panel data that covered 30 China's provinces from 2006 to 2018. First, China's provincial digital development index is constructed to measure the level of digitalization, and regional eco-efficiency is estimated by a non-radial data envelope analysis (DEA) model. Based on that, the panel data regression model and panel vector autoregression (PVAR) model are used to explore the direct effects and dynamic effect of digitalization and financial development on eco-efficiency, respectively. Then, the threshold regression model is employed to check the threshold effect of the two variables on eco-efficiency. The following conclusions are drawn: (1) Both digitalization and financial development have a significantly positive correlation with regional eco-efficiency, indicating that China's digitalization and financial development in recent years have both improved regional eco-efficiency. (2) Eco-efficiency has positive and longer responses to the impulse coming from digitalization and financial development, and the response of ecological efficiency to financial development is greater than its response to digitalization. (3) Threshold effects exist in the impact mechanism of digitalization on regional eco-efficiency. This indicates that the level of financial support is too low to promote the improvement in ecological efficiency. Eco-efficiency can be improved only to a certain extent. The research conclusions provide a policy reference for improving eco-efficiency and promoting China's green development.

The nonlinear effect of land freight structure on carbon emission intensity: new evidence from road and rail freight in China

The extensive literature has debated the varying effects of factors on carbon dioxide (CO₂) emissions. However, it has paid little attention to land freight structure (FS), including road and rail freight share, which may have different effects on CO₂ emissions. Based on the data from 6 eastern provinces in China during 2005-2019, the panel threshold model is used to explore the dynamic influence mechanism of road and rail freight share on transport carbon emission intensity (CE), respectively. The results show different nonlinear relationships between the share of road and rail freight and transport carbon emission intensity. First, the effect of road freight share on carbon emission intensity is all positive across different stages of trade openness, while such effect goes through a process of increasing and then decreasing with the level of trade openness improving. Second, the driving effect of rail freight share on carbon emission intensity exhibits a "negative-positive-negative" feature as the level of trade openness increases. Third, trade openness generates a double-threshold effect on carbon emission intensity. The differentiated nonlinear effects provide significant evidence of the modal shift from road to rail freight, which would be effective to alleviate transport CO₂ emissions.

Will green finance promote green development: based on the threshold effect of R&D investment

Deeply understanding the driving effect of green finance on green development is of great significance to promote economic transformation and realize the long-term green development. This paper uses the entropy method and undesirable-SE-SBM model to measure provincial green finance and green development efficiency respectively from 2008 to 2018. And based on the above, the panel threshold model is constructed to discuss the nonlinear relationship between green finance and green development efficiency from the first empirical verification. The results show that ① the impact of green finance on green development has a significant single threshold effect, only when R&D investment crosses 2.810 can green finance significantly promote green development efficiency, and before that, it will suppress green development efficiency. ②At present, a few provinces in China have crossed the threshold value of R&D investment, only including Beijing, Tianjin, and Shanghai, while the R&D investment of Jiangsu, Zhejiang, Shandong, and Guangdong gradually approaches the threshold value. Therefore, improving the construction of the green financial system, correctly guiding the direction of green capital investment, and strengthening the supervision of environmental information disclosure are important.

Will China's R&D investment improve green innovation performance? An empirical study

In 2020, China's R&D investment reached 2442.6 billion RMB, and it ranks second in the world, but the performance of green innovation has not proportionately improved. The question of how to promote the improvement of green innovation performance is particularly important in order to mitigate future environmental problems and issues due to rapid development of China's economy. While past research has examined the relationship between R&D investment and green innovation, they have not explicitly considered the effect of regional technological innovation level on this relationship. Hence, we fill this gap by exploring the relationship between R&D investment and green innovation performance using data from various regions in China from 2015 to 2019, under the effect of a threshold variable, namely, technological innovation. We explore the impact of economic development level, environmental regulation level, foreign direct investment, and science and technology in fiscal expenditures on green innovation performance. The empirical results show that when the regional technological innovation level is used as the threshold variable, the R&D investment has a significant double-threshold effect with the lagging three-phase green innovation performance. When the technological innovation level is low (< 0.1082), R&D investment has a negative impact on green innovation performance. Moreover, when the technological innovation level is high (>0.5837), the impact of regional R&D investment on green innovation performance is sub-optimal. Consequently, the range of [0.1082 to 0.5837] is the best range for the positive impact of R&D investment on green innovation performance. Furthermore, among China's 30 provinces and cities, 24% (mostly areas located in the southwest and northeast of China) have the technological innovation level in the optimal range. Our results help explain the current status of China's R&D investment and green innovation development, and provide a theoretical basis for the formulation of government innovation investment policies.

Research on the impact of energy technology innovation on total factor ecological efficiency

Promoting sustainable economic development from the perspective of energy technology is crucial, given limited energy resources and severe environmental pollution. Based on the panel data of China's provinces from 2000 to 2017, we empirically explore the complex relation among energy technology innovation, regional economic growth, and total factor ecological efficiency. We innovatively introduce ecological footprint as one of the input indicators of total factor ecological efficiency measured using slack-based measure-data envelopment analysis, thereby comprehensively quantifying sustainable economic development. Moreover, we adopt spatial econometric and threshold regression models to empirically assess the relation between energy technology innovation and total factor ecological efficiency. We infer the following conclusions. First, both China's provincial ecological efficiency and energy technology innovation possess significant spatial positive correlation, manifesting a spatial geographical distribution agglomerated by similar characteristics. Second, the regional energy technology innovation has a remarkable spatial effect on ecological efficiency, displaying a U-shaped trend. Compared with the direct effect, the spatial spillover effect is more intense, along with a much stronger long-term influence. Third, under the regulation of regional economic growth, two inflection points exist in the effect of energy technology innovation on ecological efficiency. Energy technology innovation is not conducive to total factor ecological efficiency under low regional economic growth. No significant relation exists between the two core variables under medium regional economic growth. Furthermore, energy technology innovation positively influences total factor ecological efficiency only when regional economic growth reaches a certain peak.

Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted "U-shaped" characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted "N" shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China's carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east-west direction) changes greatly, while the latitude of the gravity center (north-south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.

Influences and transmission mechanisms of financial agglomeration on environmental pollution

The mechanism between financial agglomeration and environmental pollution is an important concern for both academia and policymaking. The main objective of this paper is to study the nonlinear impacts of financial agglomeration on environmental pollution. A theoretical framework was first constructed based on the scale effect, structure effect, and technology innovation effect of financial agglomeration and a Copeland-Taylor endogenous growth model. Using the panel data of 281 Chinese prefectural-level cities from 2003 to 2019, a panel threshold regression model was introduced to estimate the nonlinear association between financial agglomeration and environmental pollution. Industrial smoke (dust) emissions and industrial wastewater discharge were adopted to quantify current environmental pollution in China. The results show that financial agglomeration had a significant effect on improving the environment characterized by gradient thresholds; also notable is that 68.64% of the cities crossed the threshold value, entering the decelerating phase of financial agglomeration inhibiting environmental pollution. Both upgrading industrial structure and enhancing marketization could reduce environmental pollution, whereas increasing human capital, environmental regulation, and energy consumption had a deteriorating effect. The three channels for financial agglomeration to reduce environmental pollution were revealed to be financial scale, financial structure, and financial technology innovation. Our findings provide strong evidence for policymaking in sustainable development.

Innovation and green total factor productivity in China: a linear and nonlinear investigation

The empirical conclusions regarding the influence of innovation on green total factor productivity (GTFP) are relatively mixed. Based on China's provincial panel data from 1999 to 2015, this paper uses the number of patent applications to measure regional innovation capacity, and comprehensively examines the linear and nonlinear effects of innovation on GTFP. Our results show that innovation plays a leading role in promoting GTFP growth in China in general. However, two different types of patents, invention patents, and non-invention patents have heterogeneous impacts on China's green growth under the difference of innovation level. Additionally, the relationship between innovation and China's GTFP also differs significantly before and after 2009. A further nonlinear effect analysis based on a panel threshold model reveals that the impact of innovation on GTFP is higher with the rise of human capital, knowledge stock, and financial development. However, only the appropriate environmental regulation stringency is conducive to promoting the influence of innovation on China's green growth. Overall, our findings contribute to a better understanding regarding the impact of innovation on GTFP in China.

A step towards sustainable path: The effect of globalization on China's carbon productivity from panel threshold approach

Surfacing the stress of global CO₂ emission reduction and the change into a low-emission economy has become one of the prominent economic concerns in the twenty-first century. The essence of evolving a low-emission economy is to raise carbon productivity that can be estimated as the cost-effective paybacks of CO₂ emissions. A panel threshold model was applied to approximate the threshold effect of globalization on carbon productivity under the development of human capital by using the panel data of thirty provinces of China from 2009 to 2017. The empirical findings demonstrate that China's carbon productivity increases, while economic growth shape moves towards sustainable development with low-carbon emission. Moreover, the driving force of globalization on carbon productivity is not tediously decreasing/increasing, but it has a double threshold effect of human capital. In line with this, this study finding found a single and double threshold of 9.3478 and 10.8800, respectively, as a benchmark where the relationship turns positive. The empirical findings have suggested several policy implications for the Chinese Government, policymakers, and regulatory authorities regarding this critical issue.

The inducing factors of environmental emergencies: Do environmental decentralization and regional corruption matter?

Environmental emergencies are associated with great harm, sudden onset and high publicity and cause serious damage to a broad range of ecological environmental, human health and social properties in a short period of time. Environmental decentralization can help local governments strengthen their independence and make better use of the advantages of environmental information to curb the occurrence of environmental emergencies, but regional corruption significantly weakens its effectiveness. Therefore, based on panel data of China's 30 provincial administrative regions between 2005 and 2016, this paper applies the panel threshold model to explore the relationship between environmental emergencies, environmental decentralization, and regional corruption. The results indicate that, first, environmental decentralization, environmental administrative decentralization, environmental monitoring decentralization, and environmental supervision decentralization all have a negative influence on environmental emergencies; second, as the degree of corruption in a region increases, the effect of environmental decentralization on restraining environmental emergencies decreases; and finally, there is heterogeneity in the relationship between environmental emergencies and environmental decentralization in various regions. Environmental decentralization in the eastern and western regions negatively affects environmental emergencies, while there is a positive relationship between the two in the central region.

Renewable energy technological innovation, market forces, and carbon emission efficiency

Renewable energy technological innovation (RETI) is an important way to reduce carbon emissions and achieve sustainable development. Exploring whether RETI is beneficial to the improvement of carbon emission efficiency and how the market environment affects the role of RETI on carbon emission efficiency is critical to the design of effective policies. Therefore, based on the data from 25 provinces in China from 2002 to 2015, the Tobit fixed-effect model and the panel threshold model are used to investigate the impact and the mechanism of RETI on total factor carbon performance index (TCPI) from a market perspective. The results show that: (1) RETI can effectively improve TCPI, but this effect is affected by market factors; (2) With the reduction of market segmentation or the increase of market potential, the improvement effect of RETI on TCPI is significantly enhanced; (3) The panel threshold model further verifies that the impact of RETI on TCPI has a significant single threshold effect in terms of market segmentation and market potential; (4) There is an inverted "U-shaped" relationship between market segmentation and TCPI, and the increase of market potential is conducive to the improvement of TCPI. This paper provides corresponding policy implications for China to achieve the dual goals of economic transformation and carbon emission reduction.

Do foreign direct investment and environmental regulation improve green technology innovation? An empirical analysis based on panel data from the Chinese manufacturing industry

The environmental regulation and foreign direct investment (FDI) inflow have an important impact on the progress of green technology. This study analyzes the impacts of environmental regulation and FDI on green technology innovation (GTI) based on the panel data of 13 Chinese manufacturing sectors. The results of static panel regression show that the environmental regulation has a positive impact on GTI, while the FDI has a negative impact. The results of the panel threshold model reveal that the effect of environmental regulation on GTI presents a nonlinear shape. The negative effect of FDI on GTI is strengthened when the environmental regulation exceeds its threshold. Increasing FDI inflow can inhibit the effect of environmental regulation. Meanwhile, a strict environmental regulation can enhance the inhibiting effect of FDI on GTI. The FDI inflow into high-tech manufacturing sectors has a less negative impact on GTI than the FDI inflow into low-tech sectors in the case of the enhancement of environmental regulation. This study provides some implications for the formulation of environmental regulation and the FDI inflow into China to improve the GTI.

Nonlinear influence of urbanization on China's urban residential building carbon emissions: New evidence from panel threshold model

Carbon mitigation in the urban residential building sector is critical for China to achieve its carbon peak and carbon neutral commitment. However, how urbanization affects urban residential building carbon emissions is still unclear. This study adopts the panel threshold regression model to explore the dynamic influence mechanism of the urbanization on urban residential building CO₂ emissions based on the evidence from China's 30 provincial regions during 2000-2015. Results indicate that urbanization contributes positively to the increase of urban residential building CO₂ emissions, while such degree of influence varies across different stages of income and energy structure. As for per capita income, the promoting effect of the urbanization on urban residential building CO₂ emissions is enhanced with the growth of per capita income. And the degree of such increasing effect becomes greater when per capita income exceeds its threshold value. Regarding the energy mix, the driving effect of urbanization on urban residential building CO₂ emissions is also strengthened when the energy mix crosses its threshold value, showing a "stepwise growth" feature. This study reveals the nonlinear influence mechanism of urbanization on urban building CO₂ emissions, and this is helpful in boosting the related theoretical and practical exploration on the impact of urbanization on the environment. Based on our findings, an environmentally-friendly consumption pattern should be promoted and more penetration of cleaner energies should be improved in urban households, which will be effective to alleviate the increase of residential carbon emissions.

Green research and development activities and SO2 intensity: an analysis for China

Carrying out domestic research and development (R&D) activities can improve environmental performance. However, extant studies have not conclusively indicated that R&D activities in all energy fields lead to a reduction in the SO₂ intensity. SO₂ intensity is defined as the ratio of SO₂ emissions to the GDP. Hence, green R&D activities are required. However, the strong heterogeneity between green R&D activities could have distinctive economic consequences. Thus, it is imperative to study the heterogeneity of green R&D activities on SO₂ intensity. Moreover, previous studies have ignored regional differences. Although overlooked in the literature, a technology's adsorptive ability could be a key determinant of the effects of green R&D activities on SO₂ intensity. Based on a linear analysis of China's provincial data over 2000-2016, we show that green R&D activities are instrumental in reducing SO₂ intensity. Different green R&D activities have distinct goals and contrasting statistical effects on SO₂ intensity. The empirical results show that the impact of green R&D activities on SO₂ intensity differs by region. Lastly, it is proposed that green R&D activity effects on SO₂ intensity are nonlinear by analysing a technology's adsorptive ability.

The effect of trade openness on the relationship between agricultural technology inputs and carbon emissions: evidence from a panel threshold model

The development of low-carbon agriculture systems has been a global consensus to reduce carbon emissions in the agricultural sector for addressing climate change challenges. This fact brings the need to study the agricultural carbon emissions (ACEs). Studies focusing on calculating the spatiotemporal changes of ACEs and analyzing the main factors for ACE changes have been conducted. The agricultural technology inputs (ATIs) as an important factor to influence ACEs have been identified. The traditional linear model was the commonly used method to study the relationship between ATIs and ACEs, whereas the impact of ATIs on ACEs in different areas might be complex and nonlinear due to the differences in trade openness causing different development levels of agricultural technologies. Therefore, this study aims to investigate the effect of trade openness on the relationship between ATIs and ACEs using a panel threshold model and put forward policy implications for the low-carbon agriculture development. The analysis was based on data from a panel of 31 provinces of China during 2003-2018. The results show that ATIs and ACEs increased from 2003 to 2018 and the spatial distribution of ATIs was similar to that of ACEs. The ATIs had a positive effect on ACEs with a significant single-threshold effect from trade openness. When the trade openness was below the threshold (0.1425), the positive effect of ATIs on ACEs was significant (coefficient, 0.117), whereas, when the trade openness was above the threshold (0.1425), the positive effect of ATIs on ACEs significantly decreased (coefficient, 0.062). Furthermore, industrial structure and agricultural economic development were the positive drivers of ACEs, while trade openness, education level of rural workers, R&D funding, and natural disasters had negative relationships with ACEs. The results provide valuable references for understanding ACE drivers and developing low-carbon agriculture with the consideration of ATIs and trade openness.

Decreasing China's carbon intensity through research and development activities

Research and development (R&D) activities are frequently undertaken as a powerful means to improve a country's environmental performance. However, previous studies examine R&D activities as a whole, and thus do not provide detailed information, such as the carbon intensity reduction of individual actors or stages. In addition, factors that affect regional capacity for absorbing technology (absorptive capacity) are important to influence the role of R&D in carbon intensity, but they have largely been ignored in the literature. In this study, we investigate the impacts of domestic R&D on carbon intensity by classifying R&D into three stages and three actors. The results derived from a Chinese provincial panel dataset from 2000 to 2016 indicate that domestic R&D is effective for improving carbon intensity. R&D at different stages and involving different actors have statistically different impacts on carbon intensity. Further analysis using the panel threshold model provides new evidence that the nexus between carbon intensity and domestic R&D is nonlinear. Technology absorptive capacity, which is denoted by the full-time equivalent of R&D personnel, can alleviate the negative role of R&D in increasing carbon intensity and strengthen the positive effect of R&D on decreasing carbon intensity. According to the empirical evidence, some insightful policy implications for China to decrease the carbon intensity are presented.

The nonlinear effect of population aging on carbon emission-Empirical analysis of ten selected provinces in China

Existing related researches have focused on the linear relationship between population aging and carbon emissions, which easily lead to partial understanding of the effect of population aging on carbon emissions. In order to more comprehension of the effect of population aging on carbon emissions, this study explores the nonlinear relationship between population aging and carbon emission through empirical analysis of ten selected provinces in China from 2000 to 2016 using the panel threshold model. In the proposed panel threshold model, carbon emission is set as the explained variable, population aging is set as the core explanatory variable, the levels of population aging and trade openness are set as threshold variables, the levels of economic development, energy consumption structure, industrial structure, and technological innovation are set as the controlling variables, respectively. The results show that population aging has a threshold effect on curbing carbon emission. The levels of population aging and trade openness are two key factors that affect the relationship between population aging and carbon emission. Whether the level of popultion aging is lower or higher than the threshold value of 0.12937, the population aging has a negative coefficient on carbon emissions. Moreover, the higher the level of population aging, the greater the offsetting effect of population aging on carbon emission. When the level of trade openness is below the threshold value of 0.30990, the effect of population aging on carbon emission is negligible. When the level of trade openness is higher than the threshold value of 0.30990, the offesetting effect of population aging on carbon emission begins to appear. In other words, population aging has an offsetting effect on carbon emission when trade openness is in relatively high level, whereas the offsetting effect disappears when trade openness is lower than threshold value.

Will income inequality influence the abatement effect of renewable energy technological innovation on carbon dioxide emissions?

Environmental pollution and income inequality have become two salient issues in China. To achieve a green economic transformation, China urgently needs to develop renewable energy technologies to reduce carbon dioxide (CO₂) emissions. However, the relationship among income inequality, renewable energy technological innovation (RETI) and CO₂ emissions has not received sufficient attention in the current literature. Based on Chinese provincial panel data from 2000 to 2015, this paper adopts a panel fixed effect regression model and a panel threshold model to perform an analysis on the nonlinear relationship among these factors. The results show that (1) RETI is conducive to reducing per capita CO₂ emissions (PCE). However, with an increase in income inequality, the abatement effect of RETI on per capita CO₂ emissions will be hindered, and RETI will even positively contribute to PCE. (2) The panel threshold model shows that the impact of RETI on PCE has a significant single-threshold effect with regard to income inequality. When income inequality is lower than the threshold value, the impact of RETI on PCE is not significant. However, above the threshold value, that is, within the interval of higher income inequality, an increase in RETI will positively contribute to PCE. Finally, from the perspectives of income inequality and RETI, relevant policy implications are put forward for achieving the transformation of a low-carbon economy.

Health expenditure, human capital, and economic growth: an empirical study of developing countries

Social security systems were successively established in most developing countries in the 1980s and 1990s. To ensure the long-term sustainability of these newly established systems it is essential to carefully monitor the economic impact. Based on the panel data of 21 developing countries from 2000 to 2016, this paper is the first to apply the panel threshold model to empirically analyze the relationship between national health expenditures and economic growth under different levels of human capital. The results show that health expenditure and economic growth have significant interval effects because of the different levels of human capital. Specifically, when human capital levels are low, health expenditure is significantly negatively correlated with economic growth. When human capital is at a medium level, health expenditure has a positive but not significant impact on economic growth. When the level of human capital is high, the positive economic impact of the health expenditure is significantly enhanced. In addition, subgroup analyses indicate that population aging and low fertility aggravate the negative impact of health expenditures on economic growth. This study provides reliable analysis and can be used by developing countries to maintain a long-term sustainable social security system.

The influences of openness on China's industrial CO2 intensity

As a main cause of global warming, carbon emissions have received growing concerns of Chinese government. As the main sources of carbon emissions, industrial sectors account for approximately 70% of China's total emissions. In this study, the effect of openness involving foreign direct investment, together with import and export on the industrial CO₂ intensity, is comprehensively analyzed from the technology spillover perspective. Employing a panel dataset of 34 industrial sectors for the period of 2000-2010, linear regression analysis by fixed effects, the feasible generalized least squares estimator, and the Driscoll-Kraay estimator are employed. The estimation results indicate that the openness is conducive to low-carbon development except export. A further investigation conducted by employing the panel threshold model suggests that the influences of openness on the industrial CO₂ intensity are dependent on the specific characteristics of the industrial sectors, such as the domestic innovation and the level of foreign investment entering in China's industrial sectors. These findings allow the relevant policy makers to carry out insightful policies aimed at pursuing low-CO₂ intensity by considering the characteristics and situations of the local absorptive ability.

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.

Environmental regulation, economic growth and air pollution: Panel threshold analysis for OECD countries

Particles with a diameter of <2.5 μm (PM_2.5) have serious adverse-effects on human health, which have caused widespread public concern in recent decades. Currently, most of the existing research on PM_2.5 have used linear regression analysis; very few studies on the subject have been conducted using non-linear models. This study adopts a panel threshold model, which is seldom used in environmental studies, to examine the non-linear effects of environmental regulation and economic growth on PM_2.5 in 30 OECD countries, and we also explore the key driving socio-economic factors for PM_2.5 emissions. The results of our analysis show that, along with an increase in environmental policy stringency, PM_2.5 emissions first rise and then show no significant correlations, and thus a reduction in emissions can be expected if current trends continue. As for GDP per capita, significant and negative correlations are found across the three phases divided by the panel threshold model, indicating a promoting effect for PM_2.5 mitigation. In addition, public expenditure on the air sector correlated positively with PM_2.5 concentrations, expanding the share of service economy reward to reduce air pollution, and urban population ratio exhibits an inverted U-shaped pattern. Future studies may shed more light on the regulation-PM_2.5 nexus, and more studies are needed to confirm the existence of bi-directional correlations between economic development and air pollution.