The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: Evidence from the panel ARDL approach

Research on the spatiotemporal evolution and influencing factors of urbanization and carbon emission efficiency coupling coordination: From the perspective of global countries

Strategic coordination between urbanization and carbon emission efficiency (CEE) is vital for promoting low-carbon urbanization and sustainable urban planning. In order to assess the coupled coordination degree (CCD) of urbanization and CEE and investigate the factors influencing the CCD, this research employs the Super slacks-based measure (SBM) model, the coupled coordination degree model (CCDM), and the Tobit model. Four key findings emerge from the analysis of the temporal and spatial evolution traits of the CCD based on data from 106 nations worldwide between 2005 and 2020. (1) The global CEE shows a significant downward trend, and the spatial disparity is unambiguous. high CEE countries hang in the north and west of Europe, while those in Asia, Africa and the east of Europe have lower CEE. (2) The combined urbanization level and demographic, economic and social urbanization are all on an upward trend. Singapore has the greatest degree of urbanization overall globally. (3) The CCD of urbanization and CEE shows a fluctuating upward trend, with particularly strong changes in 2018-2020. 2017 and 2018 are the years with better global coupling coordination status. During the study period, the CCD results of countries are mostly uncoordinated and low coordination, and the CCD of the United States, China, India and Japan is in the front. (4) The effect of urban electrification rate on the CCD is positive; the effect of foreign trade and net inflow of foreign direct investment is negative; while energy structure and industrial structure have no significant effect. A number of policy proposals are put forth in light of the outcomes of the research to enhance the coordination.

Intricate synergistic effects between air pollution and carbon emission: An emerging evidence from China

Due to global climate change and intensifying anthropogenic pollution, China confronts the dual challenge of controlling particulate matter 2.5 μm (PM2.5) pollution and reducing carbon emissions. Quantifying the characteristics of PM2.5 concentrations and CO2 emissions, as well as identifying the driving factors and synergistic effects of PM2.5 reduction and CO2 mitigation, are crucial steps in promoting sustainable urban development and achieving the Sustainable Development Goals (SDGs) in China. In this study, we selected 168 cities as our case-study, and quantified spatial characteristics of PM2.5 concentrations and CO2 emissions from 2015 to 2020 in China. Then we analyzed driving factors affecting the spatial heterogeneity of PM2.5 reduction and CO2 mitigation applying Multi-scale Geographically Weighted Regression (MGWR) model. By employing coupling coordination degree (CCD) model, we further detected the spatiotemporal evolution patterns of the synergistic effects between PM2.5 reduction and CO2 mitigation in key Chinese cities. The result showed that: (a) From 2015 to 2020, PM2.5 concentrations experienced a significant reduction from 59.78 μg/m3 to 49.83 μg/m3, while CO2 emissions increased from 44.88 × 106 t in 2015 to 45.77 × 106 t in 2020; (b) Green economy efficiency (gee), government attention (gover), and environmental regulation (envir) demonstrate the most pronounced synergistic effect on pollution reduction and carbon mitigation, with the drivers exhibiting obvious spatial heterogeneity; (c) The overall coupling coordination level of PM2.5 pollution and CO2 emissions in China dropped from 0.49 in 2015 to 0.46 in 2020, and the coupling coordination grade in northern cities was notably higher than that in southern cities. The result enhances our understanding of spatiotemporal patterns of synergistic effects between PM2.5 reduction and CO2 mitigation, and provides the theoretical basis for policy decision-making to realize pollution decrease and carbon neutral and regional environment governance.

Exploring the determinants of methane emissions from a worldwide perspective using panel data and machine learning analyses

This article contributes to the scant literature exploring the determinants of methane emissions. A lot is explored considering CO2 emissions, but fewer studies concentrate on the other most long-lived greenhouse gas (GHG), methane which contributes largely to climate change. For the empirical analysis, a large dataset is used considering 192 countries with data ranging from 1960 up to 2022 and considering a wide set of determinants (total central government debt, domestic credit to the private sector, exports of goods and services, GDP per capita, total unemployment, renewable energy consumption, urban population, Gini Index, and Voice and Accountability). Panel Quantile Regression (PQR) estimates show a non-negligible statistical effect of all the selected variables (except for the Gini Index) over the distribution's quantiles. Moreover, the Simple Regression Tree (SRT) model allows us to observe that the losing countries, located in the poorest world regions, abundant in natural resources, are those expected to curb methane emissions. For that, public interventions like digitalization, green education, green financing, ensuring the increase in Voice and Accountability, and green jobs, would lead losers to be positioned in the winner's rankings and would ensure an effective fight against climate change.

The impact of technological innovations on the environmental Kuznets curve: evidence from EU-27

The EKC hypothesis expresses the inverted U-shaped relationship between per capita income and environmental quality. In the literature, the role of technological innovations and income inequality on pollution is a relatively recent discussion in the studies testing the EKC hypothesis. The aim of this paper is to investigate the impact of technological innovations, income inequality, exports, urbanization, and growth on CO2 emissions in EU-27. In addition, while investigating this relationship, exports and urbanization are also considered and panel vector autoregression (PVAR) analysis is applied for the 2005-2019 period. According to the coefficient estimation results, while income inequality, exports, and urbanization increase pollution, technological innovations contribute to environmental quality. Also, the results demonstrated that the EKC hypothesis is invalid in these countries and there is a U-shaped relationship between growth and emissions. The causality test results revealed the presence of unidirectional causality running from all explanatory variables to CO2 emissions. Moreover, impulse-response graphs demonstrated that the reply of emissions to the shocks in the explanatory variables is similar to the long-run coefficient results. In conclusion, all available empirical evidence for this relationship highlights that income inequality and technological innovations should be considered in policy-making processes to ensure environmental quality in EU-27 countries.

Industrialization and environmental sustainability in Africa: The moderating effects of renewable and non-renewable energy consumption

African countries have become interested in economic transformation through revamping their manufacturing sectors. However, the environmental effect of industrialization is an issue of great concern, with the need to maintain a sustainable environment in line with sustainable development goals. This study investigates the effect of industrialization on environmental sustainability in Africa, taking in to consideration the moderation effect of renewable energy and non-renewable energy consumption. Data was collected for 46 African countries from the Global Footprint Network, World Development Indicators of the World Bank and the Food and Agricultural Organization from 2000 to 2022. Robust panel fixed effects regression and generalized least squares methods were used to analyze the data. The empirical results showed that value added in manufacturing has a negative and significant effect on environmental sustainability. However, when interacted with renewable energy consumption, manufacturing exerted a positive effect on load capacity factor, indicating that the environment will be sustainable if manufacturing sector activities are powered by renewable energies. This suggests that renewable energy has the ability to propel industrial growth in Africa while sustaining the environment. The moderating effect of non-renewable energy and manufacturing is positive in the fixed effects regression and negative for the generalized least squares estimates. This suggest that fossil fuel consumption, particularly clean fossil fuels or natural gas can still drive African manufacturing without considerably harming the environment but continual use of it in to the long run will make the environment unsustainable. From the above results, this study recommends that for sustainable industrialization to take place, Africa should grow her manufacturing sector by extending the range of manufactured products from light to heavy manufactures while ensuring that renewable energy remains the major source of industrial energy supply.

Evaluating forest depletion and structural change effects on environmental sustainability in Pakistan: Through the lens of the load capacity factor

The pace of species extinction and deforestation has increased dramatically due to the substantial increase in global environmental degradation. This trend is approaching the crucial temperature threshold of 2 °C and calls for more attention. Although previous research has observed the individual impacts of forest depletion, structural change, economic growth, and urbanization on various sustainability outcomes, there has been no previous research into their interrelationships with an emphasis on the load capacity factor (LCF). Furthermore, no previous study has examined the environmental impacts of the abovementioned variables by contrasting the results of LCF and CO2 emissions in Pakistan. Therefore, this research suggests a theoretical framework that integrates these concepts, provides a roadmap for an effective and sustainable mitigation strategy for Pakistan and compares LCF results with CO2 emissions. Using the time-series data from 1970 to 2021, a unique and sophisticated dynamic Autoregressive Distributed Lag (DARDL) technique, the authors found that (i) a 1 % rise in forest depletion leads to a decline in load capacity factor by 0.026 %. (ii) A one per cent upsurge in structural change fosters environmental sustainability by raising the load capacity factor by 0.084 %. (iii) An increase of 1 % in economic growth dwindles the load capacity factor by 0.027 %. (iv) A one per cent surge in urbanization enhances the load capacity factor by 0.029 %. The findings suggest that Pakistan's Government should promote afforestation by emphasizing the constructive role of structural change in achieving environmental sustainability.

The impact of population characteristics on transportation CO2 emissions-does population aging important?

Reducing transportation CO2 emissions and addressing population characteristic changes are two major challenges facing China, involving various requirements for sustainable economic development. Due to the interdependence of population characteristics and transportation, human activities have become a significant cause of the increase in greenhouse gas levels. Previous studies mainly focused on evaluating the relationship between one-dimensional or multi-dimensional demographic factors and CO2 emissions, while few studies have reported on the effect of multi-dimensional demographic factors on CO2 emissions in transportation. Analyzing the relationship between transportation CO2 emissions is the foundation and key to understanding and reducing overall CO2 emissions. Therefore, this paper used the STIRPAT model and panel data from 2000 to 2019 to investigate the effect of population characteristics on CO2 emissions of China's transportation sector, and further analyzed the effect mechanism and emission effect of population aging on transportation CO2 emissions. The results show that (1) population aging and population quality restrained CO2 emissions from transportation, but the negative effects of population aging were indirectly caused by economic growth and transportation demand. And with the aggravation of population aging, the influence on transport CO2 emissions changed and presented a U-shape. (2) Population living standard on transportation CO2 emissions exhibited an urban-rural difference, and urban living standard was predominant in transportation CO2 emissions. Additionally, population growth is under a weakly positive effect on transportation CO2 emissions. (3) At the regional level, the effect of population aging on transportation CO2 emissions showed regional differences. In the eastern region, the CO2 emission coefficient of transportation was 0.0378, but not significant. In central and western regions, the influence coefficient of transportation was 0.6539 and 0.2760, respectively. These findings indicated that policy makers should make relevant recommendations from the perspective of coordinating population policy and energy conservation and emission reduction policy in transportation.

Unraveling the environmental Kuznets curve: interplay between [Formula: see text] emissions, economic development, and energy consumption

The study investigates the complex interplay among [Formula: see text] emissions, energy consumption, and economic growth in China, employing the Environmental Kuznets Curve (EKC) framework to analyze the dynamics from 1990 to 2022. The research contributes to the urgent need for sustainable development by filling important gaps in comprehending China's specific challenges and potential and considering the relationship between economic advancement and environmental quality. This study utilizes advanced econometric tools, including the AutoRegressive Distributed Lag model, Vector Error Correction Model, and AutoRegressive Integrated Moving Average, to comprehensively examine the complicated relationship between variables, considering both short-run and long-run dynamics. The study supports the EKC concept, suggesting that targeted measures can reduce environmental degradation as China's economy advances. Strategic policy recommendations include emission reduction targets, investments in green technologies, and promoting sustainable consumption patterns. Furthermore, transitioning from fossil fuels to cleaner energy aligns with global climate objectives. This research provides valuable insights for policymakers, emphasizing the interconnected nature of energy consumption, [Formula: see text] emissions, and economic growth in shaping China's sustainable future.

Do institutional contingencies matter for green investment?-An institution based view of Chinese listed companies

Current research in the field of environmental management has placed significant emphasis on understanding the reasons behind varying organizational responses to environmental responsibilities. Governance scholars emphasize the central role of institutional factors in shaping environmental responsibilities, primarily due to the substantial influence exerted by regulatory institutions. Drawing on institutional theory, we investigate how sub-national institutional factors impact a firm's green investment intensity and explore their moderating influence on the relationship between green investment and a firm's financial performance. Using a database of Chinese listed companies from 2012 to 2019, this study employs fixed effect model as a baseline regression. Our analysis demonstrates that sub-national institutions, such as state-owned enterprises (SOEs), regional development, and cross-listing, have significant and positive impact on corporate green investment. Our study further provide an evidence that green investment significantly improve firms' financial performance. Moreover, the positive effect of green investment on financial performance is stronger in SOEs and in firms of developed regions as compared to their counterparts, and weaker in cross listed firms than those of non-cross listed peers. Our study suggest that subnational institutions play an imperative role in improving environmental quality and financial performance by promoting corporate green investment. To make sure that our findings remain robust to endogeneity, we applied generalized method of moments (GMM) and propensity score matching (PSM) method. Our findings further provide implications for emerging economies with similar shareholding patterns and unbalanced regional development.

Clean energy production index and CO2 emissions in OECD countries

This study aimed to analyze the relationship between the energy transition index recently developed by Lau et al. (2023) and CO2 emissions in OECD countries. The topic is relevant, as the energy transition is a sustainable path to reduce CO2 emissions in countries. The CS-ARDL approach is used to estimate the short-run and long-run coefficients. Additionally, the Dumitrescu and Hurlin (2012) test is employed to determine the causal relationship between the variables. It was found that a 1 % increase in clean energy production reduces CO2 emissions by 0.33 % and 0.23 % in the short and long run, respectively. Furthermore, a bidirectional causal relationship exists between CO2 emissions and the energy transition indicator. This suggests that policies implemented by OECD countries to reduce carbon emissions will inevitably entail the adoption of cleaner energy sources.

Impact of transportation infrastructure and urbanization on environmental pollution: evidence from novel wavelet quantile correlation approach

In terms of achieving sustainable development goals (SDGs), the developing economies are facing many issues, and one of the key issues is environmental degradation. Being a developing economy, Pakistan is also experiencing thought-provoking impacts of global warming and still far away from the ideal track of sustainable development. For addressing environment-related issue and achieving the targets of SDGs, a policy-level reorientation might be necessary. In this view, this study investigates the impact of economic growth, transport infrastructure, urbanization, financial development, and renewable energy consumption on CO2 emissions by using the data of Pakistan during 1990-2020. For this purpose, we use novel wavelet quantile correlation approach. The empirical results of wavelet quantile correlation approach demonstrate that economic growth, transport infrastructure, urbanization, and financial development are responsible for environmental pollution. Whereas, result also claims that renewable energy consumption is a useful tool for reducing environmental pollution in Pakistan. Moreover, the results of FMOLS approach show that 1% increase in economic growth, transportation infrastructure, urbanization, and financial development increases CO2 emissions by 0.240, 0.010, 0.478, and 0.102%, respectively. However, 1% increase in renewable energy usage reduces CO2 emission by 1.083%. Based on the empirical outcomes, this study proposes comprehensive policy framework for achieving the targets of SDG 7 (clean energy), SDG 8 (economic growth), SDG 11 (sustainable cities and communities), and SDG 13 (climate action).

BRICS and the climate challenge: navigating the role of factor productivity and institutional quality in CO2 emissions

The BRICS countries are important contributors to global efforts aimed at preventing a climate catastrophe. These countries account for half of the total emissions generated by the G20 nations. In this context, this paper examines the relationship between total factor productivity (TFP) and CO2 emissions (CE) in BRICS countries from 1996 to 2022, with institutional quality serving as a moderating factor. Moreover, a diverse range of methodologies was employed to address the problem of cross-sectional dependence; i.e., the CS-ARDL technique is used to analyze the relationship between variables in both the long and short-run. The AMG and CCEMG methods are employed for robustness analysis, while the Dumitrescu-Hurlin causality test is used to assess causality. Our empirical analysis demonstrates that TFP is positively associated with CE. Conversely, we find that institutional quality has a negative impact on CE. Furthermore, the study confirms that the interaction between TFP and institutional quality has a negative effect on CE. This implies that an improvement in institutional quality leads to a decrease in CE, as it strengthens the regulatory system governing CE and reduces pollution. Environmental policy must include economic flexibility and policy unpredictability in order to meet CO2 reduction targets. In addition, the study has identified bidirectional causal links between CE and variables such as TFP, institutional quality, and other control variables. According to our study, the BRICS countries should encourage digitalization and renewable energy production while preserving a reasonable standard of institutional quality since they have significant resource advantages in the renewable energy sector.

Impact of belt and road initiative policy and interacting effect of renewable energy toward carbon neutrality

The Belt and Road Initiative (BRI) represents a comprehensive developmental blueprint that has been deployed across numerous Asian, European, and African nations, aimed at fostering economic growth and enhanced regional connectivity. However, concerns have been raised about its potential impact on the environment, specifically in the context of carbon dioxide (CO2) emissions. Employing non-parametric analytical techniques, this research undertakes an empirical investigation into the relationship between economic growth (GDP), renewable energy consumption (REC), and CO2 emissions within the context of BRI participant countries, spanning the years from 2000 to 2018. The findings of this study reveal that REC exerts a pronounced and statistically significant mitigating effect on CO2 emissions, implying that an increase in REC corresponds to a reduction in CO2 emissions. In contrast, trade openness (TRADE) exhibits a positive and statistically significant influence on CO2 emissions, signifying that greater trade openness is associated with heightened CO2 emissions. However, the observed effects of GDP, fixed telephone subscriptions (FTS), and mobile cellular subscriptions (MCS) on CO2 emissions remain inconclusive, as their impact lacks statistical significance. The effect estimates of covariates on CO2 emissions using various models reveal that REC and TRADE significantly affect CO2 emissions, while GDP, FTS, and MCS still yield uncertain results. The outcomes draw attention to the necessity of implementing policies that encourage the use of REC and reducing trade openness as an efficient way of neutralizing CO2 emissions. This research provides valuable insights into the impact of the BRI on CO2 emissions and emphasizes the importance of addressing the environmental implications of this initiative. Policymakers should carefully consider these findings and develop effective strategies to foster sustainable development.

Environmental deterioration in the age of industrialization and production: do industrial competition and renewable energy reduce the ecological burden?

The modern era of globalization, economic development, and increase in manufacturing activity pose severe risks to the natural environment. In this context, industries must prioritize sustainable economic growth and development. Thus, the purpose of this study is to provide insight into industrial competition, renewable energy, economic freedom, manufacturing value added, economic growth, and carbon dioxide emissions (CO2 emissions) in the top ten high-income countries from 1997 to 2019. The results from panel cross-sectional autoregressive distributed lag (CS-ARDL), augmented mean group (AMG), and common correlated effects mean group (CCEMG) techniques revealed that economic growth and industrial production have a harmful influence on CO2 emissions. Meanwhile, industrial competitiveness, renewable energy, and economic freedom are all negatively associated with CO2 emissions. This specifies that industrial competitiveness, renewable energy, and economic freedom are favorably related to environmental sustainability by limiting CO2 emissions in the top ten high-income countries. These findings imply that governments and responsible authorities/policymakers develop strategies to reduce the environmental impact of manufacturing value addition and economic growth in the top ten high-income countries and allocate more financial resources to renewable energy and promote industrial competition.

Exploring the impact of public investment on income, energy consumption, and CO2 emissions in ASEAN: new insights from a panel cointegration approach

Policy adjustments can help strike a balance between economic growth and environmental sustainability, which has increasingly been the heart to nations and regions throughout the World. This paper examines how public investment affects economic growth, energy consumption, and CO2 emissions in eight ASEAN countries: Cambodia, Myanmar, Malaysia, Indonesia, the Philippines, Singapore, Thailand, and Vietnam. Extension of a Cobb-Douglas production function and application of panel cointegration techniques reveal bidirectional Granger causation between public investment and both private development and CO2 emissions from 1980 to 2019. Public investment Granger causes energy usage, the opposite does not hold statistically. More findings from pooled mean group estimations show a mean-reversion dynamic that corrects disequilibria by 14% yearly. State investment crowds in private sector growth, energy use, and carbon footprint. It also finds an inverted U-shaped relationship between public investment and energy consumption, and a U-shaped relationship between public investment and CO2 emissions, indicating complex regional interactions. It is suggested the implementation of public investment policies that enrich green infrastructure projects to foster growth while minimizing environmental impacts, and encourage a strategic approach to public investment for prioritizing environmental sustainability and thus, achieving Sustainable Development Goals 7 to 9 and 11 to 13 in this region.

The optimization of an EV decommissioned battery recycling network: A third-party approach

In this paper, we solve the urgent problem to construct a recycling network of decommissioned batteries of Electric Vehicles (EVs) and clarify the recycling entities that will be responsible for its reverse logistics (RL) process. We consider the third-party recycling entities to develop a recycling network and conduct a case-study of Xi'an, a key industry of EVs in China to provide a reference for the government and enterprises to develop recycling plans. We scientifically optimize our recycling network, which will have a significant impact on the environmental and economic benefits of electric vehicles (EVs) in Xi'an in the future. Specifically, we consider the costs of transportation, construction, operation, recycling, packaging, and emission, as well as the profits achieved through sales revenue and subsidy offerings. We collect the actual data of potential facility locations in Xi'an, predict the quantity of decommissioned batteries in the future, and develop a fuzzy-based model to solve the optimal results of battery traveling path and distribution in the recycling process network. Our results show that with the rapid growth of the number of decommissioned batteries, third-party revenues will reach about 53.08 billion by 2035. When the facilities split the recycling process load appropriately, the network has increase in revenue while the utilization rate of facilities will decrease. We expect that splitting will be a major trend in the future development of recycling network in Xi'an. Finally, a sensitivity analysis finds that with the environmentally conscious and safe operation of recycling, the negative impact on the third-party enterprises' revenue will be small. Our proposed methodology can serve as a critical framework for other cities and governments to plan their recycling networks and formulate regulations, reflecting on the realistic projection of the scale of decommissioned batteries of EVs and the potential siting and sizing of the recycling facilities.

Impact of energy and industrial structure on environmental quality and urbanization: evidence from a panel of BRICS countries

Global sustainable development demands boosting renewable energy and optimizing industrial structures. This study employs a panel vector autoregressive (PVAR) model to examine the dynamic relationship between energy structure, industrial structure, environmental quality, and urbanization in the BRICS countries from 1990 to 2021. Energy structure, industrial structure, environmental quality, and urbanization cointegrate empirically. Energy mix optimization and industrial structure upgrades can improve environmental quality. Energy enhancements also supported urbanization. Accelerating industrial change could adversely impact urbanization. The impulse response results demonstrate that expanding renewable energy and tertiary industries such as financial and service boost environmental quality and urbanization. The variance decomposition investigation reveals significant "path dependence" in reducing carbon emissions and increasing urbanization. Finally, based on the findings, policy insights for enhancing environmental quality and fostering urbanization are laid out and disputed, with long-term implications for environmental managers and urban planners.

The mediating role of renewable energy, sectoral output and economic growth on greenhouse gas emissions: African regional perspective

Global greenhouse gas emissions are increasing when they should be progressively reducing, given worldwide concerted emissions mitigation efforts and protocols. To effectively tackle emissions to foster a sustainable climate, the situation's complexity needs a sector- and region-specific approach, not a one-stop analysis. We must first understand where the emissions originate-which sectors contribute the most to them. This study employs a panel multiregional framework with advanced econometric techniques accounting for cross-sectional dependence and heterogeneous slope coefficients to analyse GHG emissions (CO2 and CH4), sectoral output, economic growth and renewable energy dynamics across African regions from 2010 to 2019. The empirical findings are as follows: First, regional impacts of the economic sectors vary substantially, reflecting technological and socioeconomic differences leading to heterogeneous environmental patterns in the short and long term. Second, the estimated EKC turning points are uniformly lower, indicating slower environmental impact growth with sectoral development in African regions. Third, trade and urbanization are critical drivers of emissions in most regions and economic sectors, with a more pervasive impact on CO2 emissions than CH4 emissions. Finally, sectoral output imposes differential indirect CO2 and CH4 emissions effects via renewable energy, with East African manufacturing exhibiting the most significant emissions-reduction impact. Disaggregated, regional, and sectoral-specific strategies are recommended for designing green development pathways policies.

A novel integrated optimization model for carbon emission prediction: A case study on the group of 20

Carbon emission is a central factor in the study of the greenhouse effect and a crucial consideration in environmental policy making. Therefore, it is essential to establish carbon emission prediction models to provide scientific guidance for leaders in implementing effective carbon reduction policies. However, existing research lacks comprehensive roadmaps that integrate both time series prediction and analysis of influencing factors. This study combines the environmental Kuznets curve (EKC) theory to classify and qualitatively analyzes research subjects based on national development patterns and levels. Considering the autocorrelated characteristics of carbon emissions and their correlation with other influencing factors, we propose an integrated carbon emission prediction model named SSA-FAGM-SVR. This model optimizes the fractional accumulation grey model (FAGM) and support vector regression (SVR) using the sparrow search algorithm (SSA), considering both time series and influencing factors. The model is subsequently applied to predict the carbon emissions of the G20 for the next 10 years. The results demonstrate that this model significantly improves prediction accuracy compared to other mainstream prediction algorithms, exhibiting strong adaptability and high accuracy.

How to lead on carbon neutrality through sustainable development: A perspective on renewable energy, Information and Communication Technology (ICT), and logistics networks

In the face of intensifying climate change-induced environmental problems, understanding the causal relationship between carbon dioxide (CO2) emissions and socioeconomic factors is crucial for achieving sustainable development. This study investigates how the causal relationships between renewable energy, information and communication technology (ICT), logistics networks, economic growth, industrialization, and energy intensity impact sustainable development using a panel dataset drawn from 104 countries and covering 2006 to 2019. Methodologically, panel unit root, panel co-integration, and Granger causality tests are employed as robust econometric techniques. The results of the panel unit root and co-integration tests confirm the stationarity of the variables and reveal significant long-term relationships among them throughout the empirical analysis period. Notably, the panel FMOLS and DOLS estimates indicate a positive effect of RE and ICT on reducing CO2 emissions, whereas GDP and energy intensity have a negative impact on CO2 emissions reduction. Moreover, the pairwise Granger causality test findings indicate bidirectional causal relationships between RE and CO2, IND and CO2, ICT and RE, IND and RE, GDP and ICT, as well as IND and ICT. The study recommends providing policy support, including for technological development and innovation, towards establishing a system that fosters sustainable development.

How does renewable energy, newborn birth rates, industrialization, and economic growth affect environmental quality? New evidence from 90 Belt and Road countries

Reducing carbon emissions is a critical approach for attaining global environmental sustainability and combating climate change. To investigate how energy, population, industry, and economic structure affect environmental quality. This study collects panel data for 90 Belt and Road (B&R) nations from 1995 to 2021. For the first time, the nonlinear dynamic impacts of renewable energy, newborn birth rate, industrialization, and economic growth on carbon emissions are investigated using a threshold panel model and a panel vector autoregression (PVAR) model. According to the study's findings: (1) models 1-4 demonstrate that all structural factors have substantial threshold impacts on carbon emissions, demonstrating a nonlinear connection. (2) Carbon emissions are negatively impacted by energy structure (renewable energy) and population structure (newborn birth rate). Industrial structure (industrialization) and economic structure (economic growth), on the other hand, have a beneficial influence on carbon emissions. However, when the structural variables grow in size, their threshold effects all increase this contribution. (3) In three groups of nations with varying wealth levels, differences in the influence intensity of structural factors on carbon emissions, particularly renewable energy and economic growth, were detected. The impact of renewable energy on carbon emissions is: middle-income (MI) countries > high-income countries (HI) > low-income countries (LI). The impact of economic growth on carbon emissions is MI countries > LI countries > HI countries. Based on the findings, relevant policy recommendations are provided to the policy makers of the "B&R" countries from the perspectives of structural factors and heterogeneity. It provides certain references for the realization of global environmentally sustainable development strategies and the coordinated development of economic, social and environmental systems.

Does globalization matter in the relationship between renewable energy consumption and economic growth, evidence from Asian emerging economies

The study aims to investigate the impact of social, economic and political globalization on the renewable energy-economic growth nexus in a panel of six Asian emerging economies over the period 1975-2020. The results of the CS-ARDL approach show that renewable energy consumption contributes significantly to long run economic growth. Economic and political globalization firmly hold back economic growth, while social globalization directly promotes economic growth. The nonlinear effects of political, social, and economic globalization on economic growth clearly demonstrate the validity of the inverted U-shaped relationship between political globalization, economic globalization, and economic growth, and the U-shaped relationship between social globalization and economic growth. The study also found that economic, social and political globalization moderated the impact of renewable energy on boosting economic growth. Based on the renewable energy consumption model, it is revealed that economic growth significantly promotes long run renewable energy consumption. Economic, social, and political globalization have significantly boosted long run renewable energy consumption. However, the nonlinear effect model reflects a U-shaped relationship between globalization indicators and renewable energy consumption. The interaction of political, economic, and social globalization with economic growth has also witnessed an increase in renewable energy consumption, which supports the scale effect hypothesis. The causality test concludes that there is a two-way causal relationship between renewable energy consumption and economic growth, thus supporting the feedback hypothesis. The policy implications for Asian emerging economies are discussed based on the empirical analysis of this study.

Asymmetrical analysis of economic complexity and economic freedom on environment in South Asia: A NARDL approach

The environment has become a growing concern for many countries, as pollution and other environmental degradation can harm human health, economic growth, and overall well-being. This paper probes into the asymmetrical implications of economic complexity and freedom on ecological quality in four South Asian countries from 1995 to 2019. Using Nonlinear Autoregressive Distributed Lag methodology approach, our findings indicate that carbon dioxide (CO2) emissions are intensified by economic freedom both in the long and short term, while negative and positive shocks to economic complexity increase CO2 emissions in the long term. However, a negative economic complexity shock increases CO2 emissions, whereas a positive shock has the opposite effect in the short run. Moreover, our results confirm the validity of the environmental Kuznets curve (EKC) hypothesis in the long run. Furthermore, we find that renewable energy usage and the interaction of FDI and renewable energy usage can help reduce environmental damage in both the short and long run. The findings suggest that countries should focus on attracting foreign direct investment that promotes the use of renewable energy. Additionally, policies aimed at encouraging renewable energy use should be implemented. It is important to note that as economic freedom and complexity increase, there is a corresponding increase in CO2 emissions. Therefore, South Asian policy makers are advised to prioritize the reduction in fossil fuels, the promotion of energy-saving technologies and efficient production, and trade that encourages the transition of renewable energy sources to reduce CO2 emissions.

Economic freedom and carbon emissions across the globe: the mediating effect of renewable energy consumption

A growing number of studies have examined the extent to which economic liberalization policies would influence carbon emissions. These studies have examined this relationship while neglecting the key role that renewable energy could play in this complex relationship. The study fills that gap. It aims to examine the mediated effect of renewable energy consumption in the relationship between economic freedom and carbon emissions in 138 countries worldwide over the period 1995-2018. In this perspective, the study followed a second-generation panel econometric tests. We used Driscoll and Kraay standard errors (DKSE) and common correlated effect mean group (CCEMG) estimators for baseline results. The robustness of the results was checked using fully modified ordinary least squares (FMOLS), system generalized method of moments (System-GMM), and quantile regression (QREG). Furthermore, the study mobilized Dumitrescu and Hurlin's panel causality test to examine the causal relationship between the variables under study. The results suggest that economic freedom has a direct and indirect negative effect on carbon emissions and that renewable energy consumption mediates the effect of economic freedom on carbon emissions. These results remained unchanged at the battery of robustness checks. Moreover, Dumitrescu and Hurlin's panel causality test results indicated a bidirectional causal relationship between economic freedom, renewable energy consumption, economic growth, economic globalization, and population size with carbon emissions. The various empirical findings have helped to formulate useful policy implications for policy-makers to ensure environmental sustainability.

The role of renewable energy investment and energy resource endowment in the evolution of carbon emission efficiency: spatial effect and the mediating effect

With climate change caused by massive greenhouse gas emissions emerging as an issue of global concern, it is urgent to improve carbon emission efficiency (CEE) for countries along the Belt and Road (BRI). Considering the resource endowment characteristics of green development in BRI countries, the super-efficiency slacks-based measure (SBM) model is adopted to evaluate the current status and tendency of CEE in 60 BRI countries, while the Global Malmquist-Luenberger (GML) index to quantify the spatial and temporal variation and dynamic evolution of CEE. Subsequently, from the perspective of energy development and utilization, the spatial Durbin model (SDM) and the mediating effect model are employed to empirically examine the spillover effects and driving mechanisms of renewable energy investment and energy resource endowment on CEE. Empirical results reveal that (1) from a static perspective, the CEE of BRI countries is generally poor and unevenly distributed in terms of temporal and spatial dimensions, with significant room for enhancement. (2) Referring to the dynamic level, the GML index featured a U-shaped fluctuation, with technological progress contributing to the improvement of CEE. (3) There is a significant positive effect of renewable energy investment on CEE in the home country and neighboring countries, while energy resource endowment presents a remarkable adverse correlation. Consequently, it is suggested that inter-regional cooperation among BRI countries should be strengthened to reinforce renewable energy investment, exert the technology and knowledge spillover effect sufficiently, and break the resource curse in the environmental field. (4) The mediating effect model confirms the significant mediating mechanism of technological innovation. Renewable energy investment can enhance the CEE of BRI members by promoting the positive mediating effect of technological innovation, while energy resource endowment can inhibit the local level of technological innovation and indirectly inhibit the CEE of BRI members. The findings provide new ideas on the green development and ecological sustainability of the energy industry in BRI members and other economies.

Reducing the carbon emission from agricultural production in China: do land transfer and urbanization matter?

Urbanization and land transfer have triggered a profound reform of the Chinese agricultural sector since reform and opening, leading to a continuous rise in agricultural carbon emissions. However, the impact of urbanization and land transfer on agricultural carbon emissions is not widely understood. Therefore, based on the panel data covering 30 provinces (cities) in China from 2005 to 2019, we adopted a panel autoregressive distributed lag model and a vector autoregressive model to empirically explore the causal relationship between land transfer, urbanization, and agricultural carbon emissions. The main conclusions are as follows: (1) Land transfer can significantly reduce carbon emissions from agricultural production in the long run, while urbanization has a positive effect on agricultural carbon emissions. (2) In the short run, land transfer has a significant positive impact on agricultural carbon emissions, and urbanization also has a positive impact on the carbon emissions of agricultural production, but in insignificant ways. (3) There is two-way causality between land transfer and agricultural carbon emission, and between urbanization and land transfer is the same, but urbanization is the one-way Granger cause of agricultural carbon emissions. Finally, some suggestions are provided for low-carbon agriculture development: the government should encourage the transfer of land management rights and guide high-quality resources to gather in green agriculture.

Probing environmental sustainability pathways in G7 economies: the role of energy transition, technological innovation, and demographic mobility

Global warming remains the most devastating environmental issue embattling the global economies, with significant contributions emanating from CO₂ emissions. The continued rise in the level of greenhouse gas (GHG) emissions serves as a compelling force which constitutes the core of discussion at the recent COP26 prompting nations to commit to the net-zero emission target. The current research presents the first empirical investigation on the roles of technological advancement, demographic mobility, and energy transition in G7 pathways to environmental sustainability captured by CO₂ emissions per capita (PCCO2) from 2000 to 2019. The study considers the additional impacts of structural change and resource abundance. The empirical backings are subjected to pre-estimation tests consisting of cross-sectional dependence, second-generation stationarity, and panel cointegration tests. The model estimation is based on cross-sectional augmented autoregressive distributed lag, dynamic common correlated effects mean group, and augmented mean group for the main analysis and robustness checks. The findings reveal the existence of EKC based on the direct and indirect effects of the components of economic growth. The indicators of demographic mobility differ in the direction of influence on PCCO2. For instance, while rural population growth negatively influences PCCO2 in the short-run alone, urban population growth increases PCCO2 in the short-run and long-run periods. Nonrenewable energy, information computer technology (ICT) imports, and mobile cellular subscriptions serve as positive predictors of PCCO2, while ICT exports and renewable energy moderate the surge in PCCO2. Policy implications that enhance environmental sustainability are suggested following the empirical verifications.

The nexus between economic growth, health expenditure, environmental quality: a comparative study for E7 countries

The motivation of this study is to analyze the relationship between economic growth, health expenditures, environmental pollution, gross fixed capital formation and labor force by using annual data of E7 countries for the period 2000 to 2018. The co-integration coefficient of the variables was analyzed using the PMG technique, and the causality relationship between the variables was analyzed using the Emirmahmutoglu F, Kose N. Testing for granger causality in heterogeneous mixed panels. Econ Modell 2011;28:870-6 technique. In the empirical findings, the elasticity coefficient of health expenditures, environmental pollution, gross fixed capital formation and labor force variables is positive and significant. On the other hand, in the results of the causality relationship, it was concluded that economic growth and health expenditures are causal. It is concluded that economic growth is causal to CO₂ emissions. In addition, CO₂ emissions are the cause of health expenditures. As a result of the empirical findings obtained, the implementation of policies that will reduce environmental pollution in the perspective of sustainable growth will also affect health expenditures.

Does excessive energy utilization and expansion of urbanization increase carbon dioxide emission in Belt and Road economies?

The phenomenal increase in global temperature and variation in climate change are the replications of nature, alarming governments to limit the emissions of greenhouse gases (GHGs) and adopt green innovation and environmental-friendly clean and green technologies. In this paper, we empirically investigate whether there are any changes in excessive consumption of energy from conventional sources, expansion of urbanization, carbon dioxide (CO₂) emissions, and economic growth in six different regions, namely, East Asia (EA), South Asia (SA), Southeast Asia (SEA), Central Asia (CA), Eastern Europe (EE), and the Middle East and North Africa (MENA), under the Belt and Road Initiatives of panel data over the period of 1985 to 2017. The empirical methods include a panel co-integration check, heterogeneity test, panel Granger causality test, pooled mean group (PMG), and augmented mean group (AMG). To verify the outcomes, robustness tests were carried out using the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Our results confirm that CO₂ emissions are primarily influenced by excessive utilization of conventional energy, economic growth, and expansion of urbanization. The findings confirm the co-integrating relationships among the variables in all six regions. Moreover, the panel causality analysis identified a bidirectional causal relationship between energy consumption, economic growth, urbanization, and CO₂ emissions. While these results can play an instrumental role in formulating CO₂ emission policies among our selected countries, our research can also assist policymakers and governments in other developing countries implement important policy initiatives. In this regard, the findings suggest that the current environment-related polices of Belt and Road Initiatives (BRI) do not efficiently tackle CO₂ emissions. In order to achieve the CO₂ emission degradation objective, the Belt and Road countries should restructure their environment-related policies by limiting the consumption of conventional energy and expansion of urbanization. The adaptation and establishment of such a panoramic policy program can assist emerging economies to acquire consolidated and environmentally sustainable economic growth.

Urbanization and energy equity: an urban-rural gap perspective

A high-speed urban expansion in China over the past two decades has been accompanied by a great leap forward for energy consumption. However, such a significant socio-economic transition may increase the potential risk of energy inequality, which deserves special attention. Using China's provincial panel data covering the periods of 1997-2020, this paper mainly studies the impact of urbanization on urban-rural electricity consumption inequality with a modified STRIPAT model. The results of the Generalized Method of Moments (GMM) estimation show that there is a significant U-shaped relationship between urbanization and urban-rural electricity consumption inequality. The estimated short-run turning point arrives at the urbanization level of around 63.54% and 61.18% for the long-run estimates. We further carry out a regional heterogeneity analysis and then have two interesting findings: firstly, the colder northern region's turning point (70.95%) arrives later than the south (57.69%). Secondly, the baseline U-shaped relationship remains for developed eastern regions and the estimated turning point is 57.91%, while for the undeveloped midwestern regions, the relationship is not nonlinear but linearly negative. As an extension, we lastly explore the mechanism underlying the U-shaped relationship, and find that the interaction of urbanization's scale and efficiency effect determines the U-shaped relationship. Our findings remind policymakers that, to narrow the urban-rural development gap, the future preference of energy policy should be dynamically adaptive to varied regions and development stages.

Enhancing environmental quality in Portugal: can CO2 intensity of GDP and renewable energy consumption be the solution?

Despite Portugal's effort to reduce carbon emissions, the country still emits about 1.6% of the European Union's CO₂ emissions. Meanwhile, limited empirical studies have been undertaken in the context of Portugal. As a result, this study examines the asymmetric and long-term impact of CO₂ intensity of GDP, energy consumption, renewable energy and economic growth on CO₂ emissions in Portugal from 1990 to 2019. The nonlinear autoregressive distributed lag (NARDL) is employed to uncover the asymmetric connection. The findings show that there is nonlinear cointegration among the variables. The long-run estimation reveals a positive change in energy consumption positively affects CO₂ emissions, while a negative shock in energy consumption has a neutral effect on CO₂ emissions. Furthermore, positive shocks of economic growth and CO₂ intensity of GDP enhance environmental deterioration by increasing CO₂ emissions. Meanwhile, the negative shock of these regressors positively impacts CO₂ emissions. In addition, positive shocks in renewable energy enhance environmental quality, while negative shocks in renewable energy increase environmental deterioration in Portugal. Policymakers should focus on reducing per-unit energy usage and CO₂ efficiency gains, which would necessitate a significant reduction in CO₂ intensity and energy density of GDP.

Multi-scale analysis of China's transportation carbon emissions based on nighttime light data

This study explores the spatial and temporal evolution characteristics of transportation carbon emissions from multiple scales. Based on the integrated DMSP/OLS-NPP/VIIRS nighttime light data, a transportation carbon emission estimation model was constructed, and the spatial and temporal evolution characteristics of transportation carbon emissions in 30 provinces and some counties in China from 2000 to 2019 were analyzed. The main findings are as follows: (1) The goodness-of-fit of the estimation model improved from 51.2 to 87.15% by introducing the GDP variables. (2) At the provincial scale, the provinces with high carbon emissions from transportation were mainly distributed in the eastern region, with the highest value increasing from 19,171.6 million tons in 2000 to 71,545.98 million tons in 2019. The spatial distribution has a significant and positive spatial spillover effect, and the H-H aggregation was mainly distributed in the east-central region, showing a trend of expansion from the coast to the inland. Trend analysis showed that Shandong, Guangdong, Shanghai, and Jiangsu were areas with a rapid growth of high carbon emissions. (3) The county scale displayed a northeast-southwest evolutionary pattern, with the center of gravity in Henan. The spatial distribution showed a significant spatial agglomeration phenomenon. Trend analysis indicated that the transportation carbon emissions in 184 counties need to be controlled urgently, which was the focus of carbon emission reduction. This paper theoretically enriches the measurement method of transportation carbon emissions and overcomes the problem of insufficient spatial information of statistical data. In practice, it provides a scientific basis for accurate emission reduction and low-carbon development of transportation.

Investigating the rationale for low-carbon production techniques in agriculture for climate change mitigation and fostering sustainable development via achieving lowcarbon targets

In China, agricultural activities are a major source of greenhouse gas emissions, ranking second only to another significant source. This presents a significant obstacle to reducing emissions and jeopardizes both the availability of food and the sustainable growth of agriculture. It is primarily the farmers who utilize cultivated land and are thus accountable for the initiation of these emissions. Farmers' role is significant in adopting green and low-carbon (LC) agricultural production practices, and their actions are directly tied to the achievement of the dual goals of carbon reduction. Understanding their motivations for engaging in LC production and the factors that influence their willingness to do so is important for both theory and practice. In this study, data was collected from 260 questionnaires in 13 counties across five major cities in Shaanxi Province. The purpose was to identify factors that impact farmers' motivation and willingness to engage in LC agriculture using linear regression analysis. A structural equation model was constructed to better understand the underlying mechanisms that influence farmers' actions towards LC farming practices. The study's findings indicate that (1) farmers' behavior towards LC production practices is notably impacted by internal motivation based on joy and internal motivation based on responsibility (IMR); (2) IMR has the most pronounced effect on farmers' adoption of LC production practices; (3) the internal motivation based on joy, IMR, behavior attitude, subjective norm, and perceived behavior control are related to each other; and (4) the multi-group analysis of the data indicates that the impact of internal motivation based on joy and IMR on adopting sustainable farming practices may vary among different groups. It is essential to support farmers who have strong intrinsic motivation to engage in sustainable agriculture. Additionally, policymakers must promote positive attitudes towards sustainable farming to achieve the desired environmental (LC) objectives.

The symmetric and asymmetric impacts of green energy, eco-innovation, and urbanization in explaining low-carbon economy for Pakistan

Over the past three decades, global economic development patterns have considerably affected the natural environment, and economies have endured a plethora of environmental concerns as a result of the negative effects of climate change. Among them, Pakistan is the fifth most vulnerable country, and climate change has harmfully affected the ecological and socio-economic conditions of the country. In this regard, this study aimed to investigate the role of green energy consumption, eco-innovation, and urbanization while explaining the dream of low-carbon economy and environmental sustainability in the context of Pakistan using annual time series dataset spanning from 1990 to 2020. The short-run and long-run associations among explained and explanatory variables were investigated using the symmetric, asymmetric, and quantile autoregressive distributed lag models. The findings of the study demonstrated that low-carbon economy, green energy consumption, ecological innovation, urbanization, GDP per capita, and labor force are cointegrated for the long-term association in symmetric, asymmetric, and quantile autoregressive distributed lag models. Furthermore, green energy consumption and effective eco-innovation are the most important paths to ensure environmental sustainability, while urbanization, GDP per capita, and labor force contribute negatively to the low-carbon economy. The findings of the study provide a policy framework for the development of a comprehensive strategy to promote environmental sustainability in Pakistan by emphasizing green energy consumption, ecological innovation, and controlled urbanization, as well as the incorporation of environment friendly policies into economic development policies.

Assessing freight structure and its effect on transport CO2 emissions: heterogeneous and mediating effect analysis

Understanding the effect of freight structure (FS) on carbon dioxide (CO₂) emissions is significant for policymakers in the transport sector. However, few studies have investigated fully how the freight structure influences transport CO₂ emissions. In the paper, a comprehensive freight structure index is proposed to assess the freight structure in 16 provinces of China during 2005-2019. Then, the heterogeneous effect of the freight structure and other factors on CO₂ emissions are quantified by the quantile regression model developed. The mediating effect between freight structure and CO₂ emissions is further examined. And the panel causality test is used to explore the directions of causality among variables. The main results indicate that (1) the level of freight structure shows a distribution of increase from western and central regions to eastern regions in 16 provinces. (2) Freight structure has a positive effect on transport CO₂ emissions, and its effect is significantly heterogeneous in all quantiles. (3) Freight structure has an indirect impact on CO₂ emissions through the scale effect. (4) The bidirectional causality is confirmed between economic growth, industrial structure, and information and communication technology with carbon emissions. A unidirectional causality from CO₂ emissions to freight structure is also found. Finally, some policy recommendations are proposed for sustainable transport development in China. The results facilitate the formulation of the regionally tailored carbon emission reduction strategy in the transport sector.

The relative roles of domestic and foreign capital in aggregate production of GDP and CO2-equivalent emission across OECD countries

This paper adopts a two-stage stochastic frontier analysis framework to analyse the roles of foreign and domestic capital in the aggregate production of gross domestic product (GDP) and CO₂-equivalent emissions across 36 OECD countries from 1990 to 2014. The first stage estimates a quadratic output directional distance function to derive the marginal products of foreign and domestic capital with respective to GDP and emissions. The second stage examines explanations for variations in the marginal rate of technical substitution (MRTS) of foreign and domestic capital across OECD countries. Our paper finds two important empirical evidence findings on the role of foreign capital in the aggregate production of desirable and undesirable outputs. Firstly, that foreign capital appears to be more effective than domestic capital in generating GDP and curbing CO₂-equivalent emissions. We find that one standard deviation of GDP (or $2333 billion in 2011 dollars) would require $1857 billion (in 2011 dollars) of foreign capital in comparison with $4867 billion of domestic capital, ceteris paribus. On the other hand, the reduction of CO₂-equivalent emissions by one standard deviation would demand $4091 billion (in 2011 dollars) of foreign capital relative to $16,539 billion of domestic capital. Second, foreign capital is more effective in reducing emissions in countries characterised by higher GDP per capita, larger population density, and higher shares of manufacturing sectors and exports.

Spatial-temporal characteristics and decoupling effects of China's transportation CO2 emissions

Both the realization of the "double carbon" goal and the low-carbon economy development requires a focus on transportation CO₂ emissions. Calculating Chinese transportation CO₂ emissions and exploring its principles are essential for achieving high-quality development of the transportation industry. Firstly, we use a "top-down" method to assess carbon emissions from transportation operations from 2003 to 2019. Secondly, the study decomposes the influencing factors of transportation CO₂ emissions in China using the log-average weight decomposition method. Thirdly, the Tapio decoupling model is applied to study the decoupling effect of transportation CO₂ emissions in each province of China. The findings suggest that China's transport carbon emissions are growing at an annual rate of roughly 16%. All GDP per capita, transportation energy intensity, and population size increase the growth of transportation CO₂ emissions. Contrastly, energy use per unit of turnover and transportation intensity decrease the growth of transportation CO₂ emissions. There is much variation in China's carbon emission decoupling index from year to year. Policy recommendations are proposed in response to the study of the above findings and the differences in carbon reduction potential among provinces.

The role of renewable energy consumption on environmental degradation in EU countries: do institutional quality, technological innovation, and GDP matter?

In the face of climate change and environmental degradation, reducing emission of greenhouse gases has become a key factor for environmental sustainability. Therefore, the present research is intended to explore the roles of renewable energy consumption, institutional quality, technological innovation, and GDP on carbon dioxide emissions in the 14 EU countries. In doing so, this study employed novel method of moments quantile regression (MMQR) using annual data from 2000 to 2019. Also, a number of other estimators were applied for robustness check including the fully modified ordinary least square (FMOLS), the dynamic ordinary least squares (DOLS), and the fixed effect ordinary least square (FE-OLS). The empirical findings indicate that renewable energy consumption significantly reduces CO₂ emissions across all quantiles (0.1-0.9). Furthermore, institutional quality and technological innovation improve environmental quality in 0.1-0.7 quantiles, although GDP enhances carbon emissions significantly in all quantiles. In addition, the FMOLS, DOLS, and FE-OLS results confirmed the MMQR results. The outcomes of this study suggest insights for the policymakers to mitigate carbon emissions through promoting innovative technologies for environmental protection and investing more in the development of renewable energy.

Heading towards sustainable environment: does renewable and non-renewable energy generation matter for the effect of industrialization and urbanization on ecological footprint? Evidence from China

This study examines how renewable and non-renewable energy generation interacts with both to affect the ecological footprint in China during 1990-2019 by using FMOLS, DOLS, and CCR estimation techniques and ARDL simulation models to assess the impact of industrialization and urbanization on environmental sustainability based on the environmental Kuznets curve hypothesis model framework. Firstly, the findings verify the applicability and validity of the EKC hypothesis in China. Secondly, renewable energy generation, industrialization, and urbanization facilitate the reduction of ecological footprint and the improvement of environmental quality in the long run, while non-renewable energy generation increases the ecological footprint and leads to the intensification of ecological pollution. However, the short-term estimates give evidence that industrialization, urbanization, and renewable and non-renewable energy generation can all increase the ecological footprint, which is not conducive to ecological sustainability. Thus, from the perspective of ecological sustainability in China, our findings are important in that they provide clear directions for ecological policy formulation, and we also provide some targeted policy recommendations for them to promote sustainable development as a goal.

Economic policy uncertainty and environmental degradation: the moderating role of political stability

The sustainability of the natural environment has become a serious policy agenda across the world. In this essence, the literature has discerned many macroeconomic factors that are causing environmental degradation. Among others, the economic policy uncertainty (EPU) is another factor that can uplift CO₂ emissions but was ignored in contemporary literature. Given that, the current analysis tends to uncover the role of EPU in CO₂ emissions and how political stability moderates this relationship. The empirical analysis was arranged on 20 years of financial data (2000-2019) of BRICS economies, and fully modified least square (FMOLS) and dynamic least square (DOLS) models were utilized to establish the empirical relationship. The empirical results reveal the significant positive impact of EPU while the negative impact of political stability on CO₂ emissions. Furthermore, the moderating impact of political stability was observed in the relationship of EPU-CO₂ emissions. In long run, the higher economic policy uncertainty intensifies the emissions of CO₂ which can be reduced by promoting political stability. The empirical analysis further advocates the dynamic impact of financial development, foreign investment, economic growth, and resource rent on pollution emissions. The moderating role of political stability implies that policy officials should certainly improve the stability of political conditions within a country. Moreover, they should ensure the overall consistency in economic policies. This study allures existing literature by exploring the moderating role of political stability in EPU and CO₂ emissions nexus.

Carbon emission effects of publicly planned logistics nodes: experience from Chengdu, China

Both academics and policymakers regard publicly planned logistics nodes (PPLN) as a potential measure to promote logistics agglomeration and mitigate environmental damage caused by logistics activities. However, such measure may lead to non-linear carbon emission effect from the perspective of agglomeration economy, and empirical evidences are yet to be found to verify this inference. This paper uses DID method and mediating effect models to explore the effects and mechanisms of PPLN on CO₂ emissions with panel data from 2000 to 2017 in Chengdu, China. Our findings show that PPLN promotes the agglomeration of both small and large-scale logistics enterprises and increases carbon emissions. In addition, the agglomeration of large-scale logistics enterprises can reduce carbon emissions; the opposite is true for smaller ones. Despite the continuous diminishing of the carbon emission increase, the emission-reduction effect has not been achieved through PPLN operations. Based on the results, the study recommends that the government should appropriately determine the location and scale of PPLN, advocate green construction and industrial upgrading, and adopt differentiated regulatory and incentive measures for enterprises. Logistics enterprises can increase their investment in green technologies and strengthen cooperation with their customers.

Research on the impact of the Belt and Road Initiative on the sustainability of the resource-based economy of participating countries

As of December 2021, China has signed more than 200 cooperation documents on the Belt and Road Initiative (BRI) with 145 countries and 32 international organizations. The Belt and Road Initiative has exerted a considerable influence on the world. This research aims to explore the impact of the Belt and Road Initiative on the sustainable development of the resource-based economy of countries participating in the Belt and Road Initiative. Based on the panel data of 130 countries participating in the Belt and Road Initiative and 46 other countries from 2006 to 2019, the difference-in-difference (DID) model was used for empirical evaluation. After multiple robustness tests, the Belt and Road Initiative was found to negatively correlate with the economic dependence on the natural resources of BRI-participating countries, promoting sustainable economic growth. The heterogeneity test results show that BRI participating countries with low income and lower-middle income benefit more than countries with high income and higher-middle income. In comparison, the degree of impact has little correlation with whether they are countries along the Belt and Road. The results of the intermediary effect test show that the level of infrastructure, industrial upgrading, and technological progress has played an essential role in the process of BRI's influence on reducing natural resource dependence to promote the sustainable development of BRI participating countries.

Financial Institutional and Market Deepening, and Environmental Quality Nexus: A Case Study in G-11 Economies Using CS-ARDL

This study presents a new insight into the dynamic relationship between financial institutional deepening (FID), financial deepening, financial market deepening (FMD), foreign direct investment (FDI), economic growth (GDP), population, and carbon dioxide emissions (CO₂e) in the G-11 economies by employing a cross-sectionally augmented autoregressive distributed lag (CS-ARDL) approach during 1990-2019. The outcomes from the CS-ARDL and dynamic common correlated effects mean group (DCCEMG) models shows that financial deepening, GDP, FDI, and population degraded environmental quality both in the short run and the long run. Contrary to this, FID and FMD improves environmental quality in these countries. The government should work to maximize financial institutions (access, depth, efficiency) and financial markets (access, depth, efficiency) to reduce the CO₂e. A strong positive and in-phase correlation of CO₂e with economic growth and population is observed for G-11 countries. These results suggest policy makers should further improve financial institutions by creating opportunities for their populations. Moreover, the governments of G-11 countries should revise their foreign direct investment policies and attention should be given to import efficient means of energy production.

Impact of Accessibility to Cities at Multiple Administrative Levels on Soil Conservation: A Case Study of Hunan Province

The development of traffic infrastructure involves massive land use changes along the transportation routes and stimulates urban sprawl at transfer nodes, leading to a degradation in ecosystem services, including soil conservation. For developing countries, especially for China, it is very important to differentiate the influences between different standards of traffic infrastructure associated with the different administrative levels of the regions where they are constructed on soil conservation. In this study, we attempt to analyze the differences in the influence of accessibility at different levels on soil conservation, for the case study area in Hunan province in China. The results indicate that: (1) traffic conditions in Hunan province have witnessed continuous improvement, and the time taken to access mega-cities, prefecture-level cities, and county-level cities from various regions has been significantly reduced. (2) The total annual soil conservation in Hunan province is maintained at approximately 2.93 × 10⁹ t. However, the spatial heterogeneity shows severe degradation in regions with lower accessibility, and weak enhancement in regions with higher accessibility. (3) A negative spatial autocorrelationship exists between accessibility and soil conservation at all levels, with the increase of administrative rank of the destination making it more obvious and intense, along with an increased tendency for the spatial distribution to concentrate. (4) Building more railways and highways from prefecture-level cities with LH clusters nearby as transfer nodes, instead of the construction of national roads and provincial roads that diverge from these railways and highways, will help limit the massive expansion of construction land and soil erosion within prefecture-level cities, rather than spreading to towns of LH clusters. This research provides an important scientific basis for future regional planning and traffic infrastructure construction, and also a reference for traffic infrastructure development in other geographically similar regions on a synchronous development stage in the world.