Carbon emissions in China's urban agglomerations: spatio-temporal patterns, regional inequalities, and driving forces

Urban agglomerations are the centers of carbon emissions. However, research on sector-specific carbon emissions in different urban agglomerations is still limited. Drawing on the data of China's six urban agglomerations in 2005, 2010, and 2015, this study investigates the spatio-temporal patterns, regional inequalities, and driving forces of total, industrial, transportation, and residential carbon emissions. The study found that Beijing-Tianjin-Hebei was the total and sectoral emission center among the studied urban agglomerations. Additionally, regional carbon inequalities gradually decreased, implying a growing regional synergistic carbon pattern. The driving forces of carbon emissions, including population, GDP, energy intensity, secondary industry, tertiary industry, foreign investment, urbanization, and green coverage, varied across sectors and regions. Notably, foreign investment could lead to lower carbon emissions in less developed agglomerations like Beijing-Tianjin-Hebei, the Central Plains, and the middle reaches of the Yangtze River, whereas more developed agglomerations like the Yangtze River Delta and the Pearl River Delta benefited less from foreign investment. Besides, ChengYu has good ecological conditions and sustainable development modes, which linked urbanization and green space to reduced carbon emissions in the industrial sector. The findings can help formulate differentiated carbon policy and support sustainable development.

Simulation of emission reduction path under the path of differentiated energy transformation in China's industrial cities: a case study of Shanghai

The formulation of long-term step-by-step emission reduction plan is an important step for effective scientific emission reduction. This paper takes Shanghai as the research object, constructs PSO-LSTM model on the basis of STIRPAT model, and further constructs three dynamic policy scenarios combined with China's actual situation and makes short-, medium-, and long-term multivariate predictions for them. The study finds that only the improvement of energy consumption structure has a promotion effect on carbon emission reduction, and urbanization, industrial structure, technology level, population, and economic level all have an increasing effect, and secondly, the carbon emission reduction path of Shanghai basically achieves the core objective of steady decrease under the three scenarios. Secondly, under the three scenarios, Shanghai's carbon emission reduction path basically achieves the core objective of steady decline, but the decline in the GU scenario is more significant. It is recommended that Shanghai further adjusts its industrial structure, optimizes its energy consumption structure, promotes technological innovation and progress, and promotes the development of the circular economy model.

Which model is more efficient in carbon emission prediction research? A comparative study of deep learning models, machine learning models, and econometric models

Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econometric models and deep learning, but few works have systematically compared and analyzed the forecasting performance of the methods. Therefore, the paper makes a comparison for deep learning model, machine learning model, and the econometric model to demonstrate whether deep learning is an efficient method for carbon emission prediction research. In model mechanism, neural network for deep learning refers to an information processing model established by simulating biological neural system, and the model can be further extended through bionic characteristics. So the paper further optimizes the model from the perspective of bionics and proposes an innovative deep learning model based on the memory behavior mechanism of group creatures. Comparison results show that the prediction accuracy of the heuristic neural network is higher than that of the econometric model. Through in-depth analysis, the heuristic neural network is more suitable for predicting future carbon emissions, while the econometric model is more suitable for clarifying the impact of influencing factors on carbon emissions.

Transitioning towards a sustainable environment: the dynamic nexus between economic complexity index, technological development and human capital with environmental quality in India

This paper aims to investigate the dynamic nexus between economic complexity index (ECI), technological development (TIN), human capital (HC) and environmental quality in India for transition towards a sustainable environment. This study is based on secondary data covering the period from 1985 to 2018. For empirical analysis, this study applied "Stochastic Impacts by Regression on Population, Affluence, and Technology" (STIRPAT) model framework under the estimation of autoregressive distributed lag (ARDL) model and vector error correction model (VECM) model. The empirical findings of model 1 show ECI, TIN, HC and urbanization (URB) as the helping hands to mitigate the problem of environmental degradation by shrinking the level of EF, whereas for model 2, ECI and TIN failed to influence the CO2 emissions, but HC served as a stimulant for environmental quality enhancement by declining the level of CO2 emissions. In contrast, GDP growth and URB strengthen the CO2 emissions levels. Moreover, in VECM framework, estimated findings reveal that the covariables Granger-cause EF and CO2 emissions, inferring that causality flows asynchronously from its covariables to EF and CO2. Impulse response function (IRF) revealed that the responses in EF and CO2 emissions ascribed to changes in its covariables. The outcome of the study has some implications for environmental policy strategists to prepare sustainable environment policies and other responsible authorities for sustainable development goal (SDGs), academician and scholars. All the stakeholders involved in environmental economics and policymakers can evaluate this study to design proper policy framework with respect to the environment. There are few studies that explore the dynamic nexus between ECI, TIN and HC with environmental quality in the control environment of URB and GDP growth using the STIRPAT model for India.

Green industrial transition: Leveraging environmental innovation and environmental tax to achieve carbon neutrality. Expanding on STRIPAT model

Anthropogenic global warming strategies on carbon mitigation are driven by encouraging green innovation and using carbon taxes, yet an empirical model to validate this is non-existing. Moreover, the existing stochastic effects by regression on population, wealth, and technology (STIRPAT) model has been found to lack policy tools on taxes and institutions that cut carbon emissions. This study amends the STIRPAT model with environmental technology, environmental taxes, and strong institutional frameworks to create a new model STIRPART(stochastic impacts by regression on population, affluence, regulation, and technology) to understand the factors impacting carbon pollution using the emerging 7 economies. Using data from 2000 to 2020, the Driscoll-Kraay fixed effects are employed in this analysis to conduct evidential tests of the impacts of environmental policies, eco-friendly innovations, and strong institutions. The outcomes indicate that environmental technology, environmental taxation, and institution quality decrease E7's carbon emissions by 0.170%, 0.080%, and 0.016%, respectively. It is recommended that E7 policymakers should adopt the STIRPART postulate as the theoretical basis for policies favoring environmental sustainability. The key contribution is the amendment of the STIRPAT model and the enhancement of the market-based mechanisms, such as patents, strong institutions, and carbon taxes, to enable environmental policy to be carried out sustainably and cost-effectively.

Impact of technology and economic complexity on environmental pollution and economic growth in developing and developed countries: using IPAT and STIRPAT models

These days, the most serious threats to the global economy, society, and humans are climate change and global warming, mainly rooted in the sharp increase of economic activities and their concomitant greenhouse gas emissions. This paper aims to investigate how economic complexity and various sectors of the economy affect environmental and economic development. This study employs a modified IPAT and STIRPAT model to investigate the relationship of environmental pollution and economic development with economic complexity, economic structure, and technology in 21 MENA and 34 OECD countries between 1971-2017. Our findings show that economic complexity and industrialization positively affect economic growth in both groups of countries. However, economic complexity and industrialization affect environmental pollution in MENA and developing countries positively but in OECD and developed countries negatively. This relationship accepts the Environmental Kuznets Hypothesis for the nexus of economic complexity and environmental pollution. According to the findings, policymakers in developing countries should increase environmental considerations in their development planning. Also, developed countries should assist developing countries in their endeavors to decrease environmental contamination by supplying technology transfer and financial aid.

Environmental sustainability through aggregate demand and knowledge economy interaction-a case of very high-HDI countries

The magnitude of the economic activities is immense in very high-Human Development Index (HDI) countries, leading to environmental degradation, a crucial problem. This study is aimed at testing aggregate demand's role in the environmental Kuznets curve (EKC) perspective and explores the role of four pillars of the knowledge economy, viz., technology, innovations, education, and institutions, as proposed by World Bank, in maintaining sustainable development of environmental quality in these countries. The analysis covers the period ranging from 1995 to 2022. The departure of normality of the variables provides a solid base for panel quantile regression (PQR). Unlike ordinary least squares (OLS) regression, which estimates the conditional mean of the dependent variable, PQR estimates the conditional quantiles. The estimated results using PQR confirm both U and inverted U-shaped aggregate demand-based EKC. In fact, these knowledge pillars in the model determine the shape of EKC. Results also reveal that two knowledge pillars, i.e., technology and innovations, are responsible for significantly reducing carbon emissions. In comparison, education and institutions are responsible for expanding carbon emissions. As a moderator, all knowledge pillars except institutions are shifting the EKC downward. The key lessons from these findings are that technology and innovation can reduce carbon emissions, while education and institutions may have a mixed impact. The relationship between knowledge pillars and emissions may be moderated by other factors, underscoring the need for further research. Moreover, urbanization, energy intensity, financial development, and trade openness significantly contribute to environmental deterioration.

Spatio-temporal variations, spatial spillover, and driving factors of carbon emission efficiency in RCEP members under the background of carbon neutrality

Improving carbon emission efficiency (CEE) has emerged as a critical way for Regional Comprehensive Economic Partnership (RCEP) members to promote carbon reduction in the context of climate change mitigation and carbon neutrality. The super-efficiency slacks-based measure (SBM) model, which considers non-desired outputs, is adopted to comprehensively assess the current state and trend of CEE in 15 RCEP countries from a spatio-temporal dynamic perspective, and the global Malmquist-Luenberger (GML) index is coupled to quantify the spatial and temporal differences and dynamic changes. Following that, taking into account the spatial characteristics of CEE, the extended STIRPAT model and the spatial Durbin model are combined to further investigate the primary influencing factors of CEE. It is found that (1) the CEE of RCEP members is generally poor and unevenly distributed in temporal and spatial dimensions, with significant room for improvement and an overall positive spatial autocorrelation; (2) CEE varies considerably among RCEP members, with developed countries far outstripping developing countries in terms of both the current status and trend of CEE; (3) on a dynamic level, the GML index exhibits W-shaped fluctuations, with technological progress acting as the dominant force; and (4) in terms of spillover effects, affluence and economic agglomeration inhibit CEE enhancement, whereas technology level and investment capacity facilitate it. The findings will be useful in developing carbon-neutral plans for various countries as well as coordinated sustainable development for RCEP regions.

Comparing the environmental impacts of nuclear and renewable energy in top 10 nuclear-generating countries: evidence from STIRPAT model

The purpose of this study is to assess the impact of nuclear energy and renewable energy on CO₂ emissions in major top 10 nuclear-generating countries based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model from 1993 to 2018. For comparison, the impact of renewable energy on emissions is also examined. For robust checking, four models would be used. The cross-sectional dependence (CD) test reveals the existence of CD in the panel data. Stationary tests indicate the selected variables have no unit root in 1st difference, and cointegration tests confirm the time series data in four multivariable models are long-run cointegrating relationship in each model. Fully modified ordinary least squares (FMOLS) and augmented mean group (AMG) are employed to estimate the long-run coefficients of independent variables, which reveals the positive impacts of variables on emissions. One percent increase in population, economic growth, carbon intensity, and nuclear or renewable energy consumption can lead to 0.984 ~ 1.060%, 1.001 ~ 1.012%, 1.000 ~ 1.011%, 0.009 ~ 0.011%, or 0.003 ~ 0.005% increase in emissions, respectively. Dumitrescu-Hurlin (DH) panel Granger causality test reveals that the causalities between the variables are mixed. Finally, some implications are proposed, such as limiting population quantity and improving the population quality, implementing a green economy, and developing safe nuclear and renewable energy.

Urbanization and CO2 emissions in Belt and Road Initiative economies: analyzing the mitigating effect of human capital in Asian countries

Balanced and sustainable development is one of the Belt and Road Initiative (BRI) objectives. Therefore, considering the role of urbanization and human capital as critical elements for sustainable development, we analyzed the moderating effect of human capital on the relationship between urbanization and CO₂ emissions in Asian member countries of the Belt and Road Initiative. In doing so, we used the STIRPAT framework and the environmental Kuznets curve (EKC) hypothesis. We also employed the pooled OLS estimator with the Driscoll-Kraay's robust standard errors, the feasible generalized least squares (FGLS), and the two-stage least square (2SLS) estimators in the case of 30 BRI countries for the period 1980-2019. The relationship between urbanization, human capital, and carbon dioxide emissions were examined first by showing a positive correlation between urbanization and carbon dioxide emissions. Secondly, we showed that human capital mitigated the positive effect of urbanization on CO₂ emissions. Next, we demonstrated that human capital had an inverted U-shaped effect on CO₂ emissions. Specifically, a 1% increase in urbanization rose CO₂ emissions by 0.756%, 0.943%, and 0.592% following the Driscoll-Kraay's OLS, the FGLS, and the 2SLS estimators, respectively. A 1% increase in the combination of human capital and urbanization reduced CO₂ by 0.751%, 0.834%, and 0.682%, respectively. Finally, a 1% increase in the square of human capital decreased CO₂ emissions by 1.061%, 1.045%, and 0.878%, respectively. Accordingly, we provide policy implications on the conditional influence of human capital in the urbanization-CO₂ emission nexus for sustainable development in these countries.

Renewable energy and CO2 emissions in G7 countries: does the level of expenditure on green energy technologies matter?

Promoting green energy is generally considered a crucial way to mitigate energy-related CO₂ emissions. However, the level at which a country's expenditure on green energy technologies interacts with renewable energy consumption to save the planet has been ignored in the literature. Within the context of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, this study investigates the interaction effect of renewable energy and expenditure on green energy technologies in mitigating CO₂ emissions in G7 countries over the period 1990-2017. The empirical results based on the Method of Moments-Quantile Regression (MM-QR) with fixed effects suggest that renewable energy and expenditure on green energy technologies have a negative and heterogeneous effect on CO₂ emissions. The interaction term has a stronger negative and heterogeneous effect across quantiles distribution of CO₂ emissions. This suggests that the extent to which renewable energy exerts downward pressure on CO₂ emissions is dependent on countries' expenditures on green energy technologies. In addition, the effect of the interaction term is stronger in countries with lower levels of CO₂ emissions. Given these findings, the study suggests the need to promote investment in green energy technologies as a catalytic converter to mitigate CO₂ emissions.

The paradigms of transport energy consumption and technological innovation as a panacea for sustainable environment: is there any asymmetric association?

Technological innovations have a great potential to develop the transportation system as more efficient, intelligent, connected, and sustainable. Therefore, transport energy consumption fundamentally transmutes how goods and people are moved with significant effects on transport demand with related energy consumption on a sustainable environment. To this end, our research aimed at investigating the environmental performance (carbon dioxide and ecological footprint) by stochastic impact by regression through a population, affluence, and technology (STIRPAT) model, and econometric approach for estimation of transport energy consumption from 1975 to 2018 for Pakistan. Moreover, our study supports the literature by exploring the association of technological innovations, financial development, carbon damage costs, and economic growth with the environment. The linear relationships of the variables are governed by the autoregressive distributive lag (ARDL) model that interestingly explored that economic growth and energy consumption, and financial development degrade the environment and resource depletion; however, technological innovations are inclined towards cleaner technologies. For asymmetric findings, we employ the non-linear autoregressive distributive lag technique recently introduced by Shin et al. (2014). The findings validate the existence of an asymmetric relationship between transport energy consumption and environmental indicators. The policymakers' prerequisites the alternative energies apart from conventional energies in the transport sector with technological innovations in transport sector energy consumption like the electronic and hybrid vehicles for a cleaner environment.

Impact of FDI, industrialization, and education on the environment in Argentina: ARDL approach

Purpose

This study employed the stochastic implications of regression on population, affluence, and technology (STIRPAT) methodology between 1972 and 2021. The main goal of this research is to look at how FDI, population growth, industrialization, and education affect the environment in Argentina.

Methodology

The F-bound test and Johansen cointegration test are employed in this research to determine if there is a co-integration association among the variables. Additionally, the Autoregressive Distributed Lag (ARDL) method is used to examine the short-run and long-run elasticity of the independent variable. This study also incorporated a pairwise Granger causality test to determine the direction of causation between the variables. After that, the study applied several diagnostic and stability tests.

Findings

The empirical evidence demonstrates the presence of a co-integration association among CO₂ emissions, population, industrialization, and education. The findings indicate that population growth and industrialization harm the environment in Argentina in the long run. In addition, a significant inverse association was obtained between CO₂ emissions and educational expenditures in the short run.

Practical implications

The existence of STIRPAT suggests that Argentina is capable of achieving sustained economic growth. To achieve the goal, countries must implement appropriate government policies and ensure their implementation. This paper argues strongly for more investment in education, renewable energy, sustainable industrialization, and research and development, all of which are essential for a green economy.

Development of an extended STIRPAT model to assess the driving factors of household carbon dioxide emissions in China

Although the past twenty years have witnessed China's remarkable economic development, the cost in terms of greenhouse gas emissions and a deteriorating environment has been enormous. Numerous studies have revealed the influence of household factors on household carbon dioxide emissions (HCEs) and called for a reduction of HCEs to mitigate climate change, but few have focused on assessing the most significant household driving factors of HCEs. Using statistical data between 2005 and 2019 in Jiangsu, China, this study developed an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model to assess the most significant driving factors of HCEs. The results show that the most significant driving factors are household size, total population, unemployment, and urbanisation rate. The study found that HCEs are positively impacted by household size while negatively impacted by the unemployment rate. Based on the study's findings, the following suggestions are proposed to lower HCEs: (i) establish an optimal consumption concept to guide residents towards consuming reasonably; (ii) cultivate a low-carbon concept among residents and promote low-carbon emissions living; and (iii) pay close attention to population structure factors and formulate effective measures accordingly. The study provides insightful information on the key driving factors of HCEs, which can facilitate achieving carbon emissions neutrality.

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu's ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The "H-H" agglomeration is mainly concentrated in Lianyungang and Suqian, while the "L-L" agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, -0.127%, -0.116%, 0.192%, -0.110%, -0.114%, and -0.123% in Jiangsu's ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

Peaking Global and G20 Countries' CO2 Emissions under the Shared Socio-Economic Pathways

Mitigating climate change requires long-term global efforts. The aim of this study is to simulate the possible paths of CO₂ emissions in G20 countries and the world from 2020 to 2050, by using the STIRPAT model and SSP scenarios with different constraints (SSP baseline, SSP-3.4). The results show that: (1) the world's CO₂ emissions cannot peak in the SSP baseline scenarios, but can peak in the SSP-3.4 scenarios through four paths other than the high fossil energy consumption path; (2) for G20 countries, in the SSP baseline scenarios, 13 countries such as China, the United States, and the United Kingdom can achieve the peak, while six countries such as Argentina, India, and Saudi Arabia cannot. In the SSP-3.4 scenarios, Saudi Arabia cannot achieve the peak, while other countries can achieve the peak, and most of them are likely to achieve significant CO₂ emission reductions by 2050; (3) climate goals have a crowding-out effect on other sustainable development goals, with less impact on developed countries and a greater impact on developing countries; and (4) the optimization of the energy structure and a low energy intensity can greatly advance the peak time of CO₂ emissions.

Population density and energy consumption: A study in Indonesian provinces

As the world's fourth most populous country, the population growth rate in Indonesia is expected to stay high. Owing to a combination of high-speed urbanization and increasing population density, economic growth is predicted to increase the demand for energy consumption. Thus, it is crucial to understand the relationship between population density and energy consumption in any country. This study evaluates the impact of population density on total energy consumption and the disaggregated electricity and fuel consumption at the provincial level in Indonesia. It uses the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. It also employed a balanced panel data of 33 provinces from 2010 to 2018. The results indicated that population density positively impacts energy consumption for total, electricity, and fuel consumption. This study suggests that incorporating population growth into national energy plans is crucial. Additionally, reducing energy inequality and uneven spatial distribution of the population is also needed.

Analysis of agricultural greenhouse gas emissions using the STIRPAT model: a case study of Bangladesh

The agriculture sector is one of the leading emitters of greenhouse gases in Bangladesh, owing to increasing mechanization, changing population patterns and increasing cultivation of irrigation intensive crops like rice. The objective of this research is to analyze how population trends, energy use and land use practices impact the emissions of three greenhouse gases from the agriculture sector in Bangladesh. The gases studied are carbon dioxide, methane and nitrous oxide. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and ridge regression are used to analyze the drivers of emissions covering the period from 1990 to 2014. Explanatory factors of emissions are the total and rural population, affluence, urbanization, fertilizer intensity and quantity, carbon and energy intensity, irrigation, rice cultivation, cultivated land and crop yield. The findings reveal that the country's total population has a negative effect, and the rural population has a negative, nonlinear impact on the emissions of methane. Affluence affects emissions of all the gases. The energy intensity and carbon intensity of agriculture increase carbon dioxide emissions. The cultivated land area, rice cultivation quantity and crop yield increase methane emissions, while irrigated land area decreases it. Rural population, total population and urbanization have a positive linear effect on carbon dioxide and nitrous oxide emissions. Fertilizer quantity and intensity increase nitrous oxide emissions. The findings imply that increasing agricultural mechanization should be based on clean energy, and land management should be regulated to enable the country to meet its Nationally Determined Contribution (NDC) targets as well as the targets of Sustainable Development Goal (SDG) 7 of increasing the share of clean energy.

Sustainability challenges in Peru: embossing the role of economic integration and financial development-application of novel dynamic ARDL simulation

Inspired by the commitment to address the environmental challenges in Peru under the UN Sustainable Development Goals 13 (Climate Action) and its implications by 2030, therefore, this study investigates the combined role of economic globalization, financial development, and fossil fuel intensity consumption using a combination of dynamic ARDL counterfactual simulation and kernel-based regularized least squares within the context of Stochastic Impact by Regression on Population, Affluence and Technology over the period 1971-2017. This research output confirms the inverted-U-shaped hypothesis between economic growth and carbon emissions. In contrast, the kernel-based regularized least squares confirms the scale effect and fossil curse hypothesis in the relationship between financial development and carbon emission, and heterogeneous effects in economic integration and carbon emission. We further document that financial development, fossil fuel consumption, urban population, affluence (economic growth), and government final consumption expenditure spur environmental pollution while economic integration reduces it. This study recommends Peru to instill environmental justice through regulations and policies restricting inflows into an exploration of environmentally unsustainable projects within Peruvian metropolises or in the Peruvian Amazon. There is a need to revisit finance and investment laws and increase investment in low-carbon infrastructure within Peru.

Green Supply Chain Management With Sustainable Economic Growth by CS-ARDL Technique: Perspective to Blockchain Technology

Logistics plays a major part in any country's or region's economic success. Logistics performance depends upon the trade between other countries and urbanization. Urbanization has major role in logistics performance. However, being a significant energy user, logistics has negative consequences. As the logistics performance increases, carbon emissions increase as well because of more transportation and urbanization. Logistics performance has positive effects related to trade openness which reduces carbon emissions. As a result, it is necessary to understand function of logistics from both economic and environmental standpoint. Logistics performance is affected by urbanization of any region. The dataset for this research is made up of 10 Asian nations with 550 observations from 2010 to 2018 and is based on the theoretical underpinnings of impact of population affluence and technology (IPAT) and stochastic impacts by regression on population affluence and technology (STIRPAT). After applying various tests like cointegration analysis, unit root test, cross-sectional dependence now long & short-term relation of variables is studied by Cross-sectionally augmented autoregressive distributed lag (CS-ARDL). As indicated by the discoveries, the logistic performance index (LPI) is basically effective on economic growth and carbon emissions, particularly when related to IPAT and STIRPAT. The findings are reviewed, and policy implications are offered, which say that current logistical infrastructure should be transformed to more environmentally friendly operations. Finally, the limits are acknowledged, as well as future research possibilities that should be pursued.

Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China

This study attempts to analyze the impact of population, property, technology, energy factors, and spatial agglomeration in the logistics industry on carbon emissions. To achieve the goal of peak carbon and carbon neutrality, the relationship between influencing factors and carbon emissions was analyzed based on panel data from the logistics industry for 30 provinces in China from 2003 to 2017 using an improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and a spatial lag model (SLM). The results show that population, property, technology, and energy factors in the logistics industry all have different degrees of influence on carbon emissions, wherein population, energy, and property have a greater influence, which implies that carbon emission reduction policies can be carried out considering the relevant aspects. In addition, under the influence of spatial agglomeration, the degree of influence of freight mileage (FM), total fixed-asset investment (TFAI), and industry population (IPOP) on carbon emissions decreases, and the degree of influence of energy intensity (EI) and industry per capita GDP (IPCG) increases. This suggests that corresponding emission reduction policies should be formulated for large urban areas based on technological innovation, infrastructure, and talent training, while smaller urban areas can focus on developing new energy and industrial economies. These findings help to complement the existing literature and provide policymakers with some insights related to urban logistics development.

Assessment of financial development on environmental degradation in KSA: how technology effect?

The discourse on the impact of financial development and its effects on environmental quality has been an important research area in the last few decades. The objective of this research attempts to test the technology effect hypothesis on environmental mitigation in the case of Saudi Arabia (KSA) over the period 1970-2016 and the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) with the Autoregressive Distributed Lag (ARDL) model used for empirical inquest. Unlike others, we insert additional variables such as technology, human capital, and the technology effects of financial development into the carbon dioxide emission model. We used the Ng-Perron unit root test to examine the stationary properties of the variables. Similarly, to examine the presence of the cointegration relationship between carbon dioxide emissions and its determinants, the Bound cointegration with multiple structural breaks approach is applied. First, The empirical findings show that financial development and technology have a negative and significant impact on environmental degradation. Second, the technology effects of financial development have an unfortunate effect on environmental mitigation. Finally, lower environmental mitigation is associated with a deepening in total population and affluence. Moreover, findings from the pairwise Granger causality test point that there is no causality running from both financial development and technology to the effect of technology among KSA. On the opposite, we looked at economic growth Granger, cause environmental quality. In addition, a unidirectional causality was seen running from environmental quality to financial development. Similarly, the relationship between affluence and financial development in KSA is unidirectional. Thus, various policy implications should be proposed to policymakers as enhancing the expansion of technology, especially in the industrial sector by incorporating renewable energy consumption to upgrade environmental quality.

Analysis of spillover effects of regional environmental pollution: an interprovincial study in China based on spatiotemporal lag model

Based on 2004-2017 Chinese interprovincial panel data, we construct the regional environmental pollution indicators, analyze the influencing factors and spillover effects of environmental pollution under the conditions of time lag, spatial lag, and spatiotemporal lag by using the STIRPAT model and dynamic spatial Durbin model, and discuss the spatiotemporal characteristics of regional environmental pollution in China. The results show that the overall regional environmental pollution in China is on the decline. Environmental pollution has strengthened the characteristics of strong in the East and weak in the West, and the characteristics of strong in the South and weak in the North began to appear. Population scale, economic growth, and industrial scale will increase environmental pollution in the region, and the environmental regulation intensity and pollution control investment will reduce environmental pollution in the region. The spillover effects of the influencing factors of regional environmental pollution in China are different. In the short term, the influencing factors have a greater impact on the neighboring areas, while in the long term, they have a greater impact on the region. Hence, the critical approach to achieving sustainable development is to give full play to the factors which can reduce environmental pollution and to effectively control the factors which will promote environmental pollution.

The drivers of energy-related CO2 emissions in Brazil: a regional application of the STIRPAT model

Since energy is one of the basic inputs for development, emerging economies should make an effort to investigate the environmental impacts of their fast economic growth. However, large emerging economies present significant regional heterogeneity that is usually uncounted for. This study uses the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model and regional data on the 27 Brazilian states to investigate the growth-CO₂ nexus under distinct development stages. To perform this analysis, we divided the states into three groups according to their average annual GDP (i.e., richer, intermediate, and poorer regions). The results suggest that richer and poorer regions, particularly, present economic and demographic developments that are environmentally costly. Also, population and per capita GDP have the largest influences on CO₂ emissions. The roles of the industrial sector and the ascending service sector are also subject to criticism. Moreover, Brazil arguably suffers from technological stagnation as its energy intensity is growing and boosting CO₂ emissions. We discuss the policy implications of these findings and suggest a future research agenda.

Pathways to progress sustainability: an accurate ecological footprint analysis and prediction for Shandong in China based on integration of STIRPAT model, PLS, and BPNN

The world has been challenged by achieving the plausible goal of sustainable development. This study aims to evaluate the ecological footprint and ecological carrying capacity and their driving factors of Shandong province in China from 1994 to 2017. Back propagation neural network method is adopted to predict the ecological footprint from 2018 to 2030. The findings are as follows: (1) The growth of ecological footprint has caused the ecological deficit in Shandong. (2) With regards to population, the increase of total population and the urbanization rate will incur the expansion of ecological footprint. (3) In terms of affluence, the elasticity coefficients of GDP per capita, the production value of industrial sectors, and the proportion of output value of the secondary industry in GDP are 0.068, 0.064, and 0.130 respectively. (4) In terms of technology, the elasticity coefficients of internal expenditure on R&D in GDP and patent number are 0.096 and 0.047 respectively, indicating that technological progress can promote ecological footprint in a short term. (6) The results of the prediction show that the ecological footprint of Shandong from 2018 to 2030 in the policy-regulation scenario is far less than that of the business-as-usual scenario. The policy recommendations are suggested to tackle the sustainable development challenges.

Income inequality, economic growth and carbon dioxide emissions nexus: empirical evidence from Ethiopia

The relationship between income inequality, economic growth, and CO₂ emissions is ambiguous both theoretically and empirically. Hence, this study examines the link between income inequality, economic growth and CO₂ emissions in Ethiopia for time span covering 1979-2014 using ARDL bounds test and DOLS approach to cointegration. The Zivot-Andrews unit root test and Clemente-Montanes-Reyes unit root test reveal that some of the variables under consideration are stationary at level while others become stationary after first differencing. Both ARDL and DOLS approaches confirm that there is a long-run relationship among the series during the study period. The long-run empirical results show that a 1% increase in economic growth accounts for a 1.05% increase in CO₂ emissions while a 1% increase in economic growth squared reduces CO2 emissions by 0.11%. The U-test result reveals that the relationship between CO₂ emissions and economic growth confirms existence of the Environmental Kuznets Curve hypothesis. The effect of income inequality on CO₂ is not robust to alternative estimation techniques; it is statistically insignificant under the ARDL estimation, but DOLS estimates show that a 1% increase in income inequality increases CO₂ emissions by 0.21% in the long-run during the study period. In the long-run, a 1% rise in urbanization, population size, energy intensity, and industrialization each positively contribute to environmental degradation in Ethiopia by 0.38%, 0.22%, 0.07%, and 0.11% respectively. Results from the Toda-Yamamoto Granger causality show a bidirectional causal relationship between CO₂ emissions and all other variables except economic growth. CO₂ emissions Granger causes economic growth with no feedback effect. Results suggested important policy implications in the light of achieving its 2030 targets of low-carbon economy for Ethiopia.

Investigating household sector's non-renewables, biomass energy consumption and carbon emissions for Pakistan

Concerns over the observed rising trend towards carbon emissions and the resulting adverse effects of climate change on human activities are the main challenges facing human beings. This study examines household sector's non-renewables and biomass energy consumption magnitude and how much carbon is emitted from non-renewable and biomass energy in Pakistan by using the PSLM 2018-2019 survey. In addition, using STIRPAT model, this study investigates the effect of income, household size, and clean energy on non-renewables and biomass energy choices of the household sector. The results show that 77% of households rely on the consumption of biomass energy. An average household uses firewood at the largest magnitude of 142.06 kg month^-1 and kerosene usage at the smallest magnitude of 4.08 kg month^-1 among non-renewables and biomass energy choices. The largest contributor to carbon on average is dang cake and its magnitude of carbon emissions is 0.87 tons household^-1 year^-1 followed by coal with a magnitude of 0.76 tons household^-1 year^-1. LPG is the lowest contributor to carbon and its carbon emission magnitude is 0.04 tons household^-1 year^-1. The income impact finding indicates that LPG, kerosene, firewood, and dang cake are necessities, whereas coal is an inferior commodity. The coefficient of household size indicates that large household uses firewood and dang cake, and small one uses LPG and kerosene. As such, households prefer to reduce non-renewable and biomass consumption by increasing clean energy. Therefore, the study suggests that to reduce non-renewable and biomass energy consumption and follow clean energy provision at household level without compromising on environmental quality. The rise in household income and reducing household size could also be a valid policy option for reducing the non-renewable and biomass energy consumption.

A technological innovation and economic progress enhancement: an assessment of sustainable economic and environmental management

This study examines the role of technological innovation and economic progress on environmental pollution by using STRIPAT and EKC theoretical frameworks in 25 developing Asian countries from the period 1998 to 2019. For technological advancement, the energy intensity has been used to gauge how much of the quantity of energy is employed to produce the additional unit of gross domestic product at domestic level. Therefore, the volume of the energy used in the production process is highly important as it is documented through the energy intensity. To capture the impact of innovation, the sum of total patent applications and trademark applications for the sampled countries has been used. This study applied second-generation unit root and panel cointegration techniques to estimate the results. To estimate the long-run relationship of variables and the cross-sectional interdependence, Pedroni Residual and Westerlund Cointegration tests are applied. Further, the Hausman-Taylor-type test has been used to check the efficiency of the pool mean group (PMG). The results of PMG regression confirm the existence of EKC in the developing Asian countries. The results of this study showed that technological development, innovations, and economic progress have the potential to reduce carbon emission and to protect the environment in developing Asian economies. Moreover, the results of error correction model indicate that in case of any external shock, this model will converge towards equilibrium within 64.6 years. The study proposed that a policy framework related to technological innovations should be sustained and the advancement of human capital and research and development should be the primary focus of the developing nations to mitigate the environmental challenges.

Sustainability assessment using STIRPAT approach to environmental quality: an extended panel data analysis

The consequence of increasing economic activities is observable in the incidence of environmental deterioration. Many studies have explored the precedents of environment quality. In this regard, the proposed stochastic impacts by regression on population, affluence, and technology (STIRPAT) and environmental Kuznets curve (EKC) analysis are valuable not only for academic analysts, but also for policymakers. This study has focused on 80 selected countries between 1990 and 2017, which confirms the existence of EKC within the STIRPAT framework. The results are estimated with the help of dynamic ordinary least square (DOLS), which controls for the autocorrelation in long periods. According to the estimated results, this study confirms U-shaped EKC based on industrial-, agricultural-, and services-based economic activities. This means that over-reliance on one specific economic activity may harm the environment and create footprint. In this regard, urbanization is responsible for affecting carbon dioxide emissions. Moreover, governance and technology are protecting the environment. This quadratic function had classified the sample countries in terms of the degree of sustainability of their economic activity sectors. This study proposes that countries should work on a balanced composition of economic activity so that the lowest possible environmental deterioration is caused.

Environmental degradation & role of financialisation, economic development, industrialisation and trade liberalisation

This paper is a pioneering endeavour to investigate the determinants of environmental degradation in Australia through a comprehensive framework of EKC and STIRPAT. Specifically, the impacts of multiple factors of socio-economic development including economic growth, trade openness, industrialization, energy consumption on CO₂ emissions are analysed. Furthermore, the influences of financial development through different dimensions (financial efficiency, access and depth) in two subsectors (financial markets and institutions) and other proxies of financial development are focused over the period 1980-2014. Empirical results show short as well as long-run differences in the association among the variables. Short-term bidirectional causality prevails between economic growth, energy consumption, industrialization, and stock market development with carbon dioxide (CO₂) emissions. However, there is no significant evidence found on EKC. This is due to the long-run positive impact of financial development, energy consumption, and trade openness on CO₂ emissions. Interestingly, the industrialization process is found to does not affect CO2 emissions. Empirical findings provide insight into why the quality of the Australian environment is truncated with frequent and widespread bushfires and suggest policymakers to have selective and strict environmental-friendly strategies to fulfil a sustainable development goal.

Export trade, embodied carbon emissions, and environmental pollution: An empirical analysis of China's high- and new-technology industries

China's export trade has been expanding steadily in recent years, significantly increasing resource consumption and environmental pollution. High- and new-technology industries are essential for achieving sustainable economic development and improving environmental quality. This study employs a multi-regional input-output model to estimate the economic benefits and environmental costs of export trade in high- and new-technology industries. Then, it analyzes the impact of economic benefits and technological levels on environmental pollution using the Stochastic Impacts by Regression on Population, Affluence, and Technology model. An input-output multi-objective linear programming model and a non-dominated sorting genetic algorithm II are adopted to combine economic development with environmental pollution and determine the optimal path for export trade. The results show that technological progress in China's high- and new-technology industries is conducive to reducing embodied carbon emissions in developed countries while increasing emissions in developing countries. Moreover, a nonlinear three-stage accompanying relationship exists between the economic benefits and environmental costs of high- and new-technology exports; this is because exports with low economic benefits generate fewer carbon emissions whereas exports with high economic benefits generate significant carbon emissions. An increase in exports with ultra-high economic benefits will generate excessive embodied carbon emissions that hinder coordinated economic-environmental development. Lastly, technological progress in the electrical and optical equipment sector can effectively promote pollution reduction; thus, it should be further developed to improve the comprehensive benefits of exports.

Reexamining the relationship between urbanization and pollutant emissions in China based on the STIRPAT model

Understanding the relationship between urbanization and pollutant emissions in China is of great significance to realizing sustainable development. Previous studies focused on the relationship between urbanization and air pollutants in China. However, the relationship between urbanization and industrial or domestic pollutants remains unclear. In this paper, we used the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to examine whether an environmental Kuznets curve (EKC) relationship exists between urbanization and pollutant emissions, including industrial wastewater, industrial SO₂, industrial soot (dust), and domestic garbage based on panel data for 277 prefecture-level cities in China from 2003 to 2015. We found that industrial soot (dust) emissions and domestic garbage increased by 83.0% and 43.5%, respectively, whereas industrial wastewater discharge and SO₂ emissions decreased by 7.4% and 10.5%, respectively. The identified inverted U-shaped relationship between the urbanization ratio (i.e., percentage of the population living in urban areas) and industrial pollutants supports the EKC hypothesis. However, the domestic garbage volume increased with increasing urbanization ratio. In the future, more attention should be paid to the prevention and control of domestic pollution. In addition, small and medium-sized cities should reduce pollutant emissions and determine effective ways to achieve sustainable development.

Dynamic Changes, Spatiotemporal Differences and Factors Influencing the Urban Eco-Efficiency in the Lower Reaches of the Yellow River

The measurement of eco-efficiency is an important tool to evaluate the level of urban sustainable development. Therefore; improving urban eco-efficiency in the lower reaches of the Yellow River ensures the implementation of ecological protection and high-quality development strategies in the Yellow River Basin. In this study; the dynamic changes of urban eco-efficiency and spatiotemporal differences in the lower reaches of the Yellow River were investigated using the Super-SBM (Super-Slack measure model) model with undesirable outputs and standard deviation ellipse. The STIRPAT (Stochastic Impacts by Regression Population; Affluence and Technology) model was introduced to analyze the factors affecting the change in urban eco-efficiency. The results showed that the overall urban eco-efficiency in the lower reaches of the Yellow River has not reached the optimal level. The overall eco-efficiency in the lower reaches of the Yellow River in Shandong Province was higher than that in Henan Province but the gap is narrowing. The spatial differentiation of urban eco-efficiency in the lower reaches of the Yellow River showed the following trends: "blooming in the middle and reverse development at both ends" in the high-value area and gradual decrease in the low-value area. From 2007 to 2018; a direction was notable with respect to the development of urban eco-efficiency in the lower reaches of the Yellow River; with the centripetal force weakening. Although the mean center of urban eco-efficiency located in Shandong Province; it notably shifted to the west during the study period. In terms of driving factors; affluence and technological progress play positive roles in driving eco-efficiency; while investment intensity; industrial structure; and foreign investment intensity hindered the optimization and improvement of urban eco-efficiency in the lower reaches of the Yellow River. The results of this study show that urban eco-efficiency in the lower reaches of the Yellow River is improving; but the regional coordination is poor. The main methods promoting the sustainable development in the study area include changing the mode of extensive investments and the introduction of foreign capital; which improve the energy efficiency and promote faster and better economic development.

The relationship between urbanization, technology innovation, trade openness, and CO2 emissions: evidence from a panel of Asian countries

This paper explores the dynamic relationship between CO₂ emissions, urbanization, trade openness, and technology innovation based on the panel data of 13 Asian countries over the period of 1985-2019. The STIRPAT model is used as a framework for the analysis. For estimation purpose, panel cointegration and FMOLS techniques are utilized. The causality between the concerned variables is also examined by estimating a panel VECM model. The results of panel cointegration reveal the presence of long-run relationship among the variables. FMOLS estimations show that energy consumption increases CO₂ emissions while technology change, urbanization, and trade openness compact it. Panel causality analysis indicates bidirectional causality between urbanization and emissions, technology and emissions, trade and emissions, and trade and technology in the long run. Overall findings support the idea that urbanization, technology innovation, and trade openness can play important role to achieve environmental sustainability.

The dynamics effect of green technology innovation on economic growth and CO2 emission in Singapore: new evidence from bootstrap ARDL approach

For an economy to excel in growth, there is usually a trade-off between financial development and environment deterioration. For a country like Singapore, which has shown a radical growth and is known for its population density, it is important to explore the role of green technology innovation in the pursuit of economic excellence with the least possible cost to the environment. By employing the novel bootstrap autoregressive-distributed lag (BARDL) technique using a time series data from 1990 to 2018, the results reported a positive and significant relationship of green technology innovation with economic growth and negative and significant relationship with carbon emissions in both long run and short run. Based on the findings, several managerial implications were discussed, whereas based on the limitations, directions for future researchers are also given.

What contributes to the regional inequality of haze pollution in China? Evidence from quantile regression and Shapley value decomposition

Against the increasingly serious haze pollution in China, this paper is to compare the impacts of different factors on haze pollution in different regions, and understand the causes of regional inequality of haze pollution. In doing so, quantile regression and regression-based Shapley value decomposition are employed in this paper. The main results are as follows. (1) Population density and industrialization level have positive impacts on haze pollution, while economic development negatively influences haze pollution, however, the impact of environmental regulation on haze pollution is ineffective. (2) With quantile increasing, the effect of foreign direct investment on haze pollution changes from positive to negative, and the influence of energy intensity on haze pollution changes from negative to positive. (3) The decomposition results specify that the regional inequality in population density is the main cause of the regional disparities of haze pollution. The inequalities in industrialization level and regional factors are also important reasons, and the contribution of energy intensity cannot be ignored either. The regional gap of economic development is conducive to reducing the regional disparities of haze pollution.

Environmental consequences of population, affluence and technological progress for European countries: A Malthusian view

This paper examines the short-run and long-run effects of economic, sociological and energy factors on environmental degradation in 28 European countries. In so doing, we employ Panel Vector Autoregressive (PVAR) and Fully Modified OLS (FMOLS) approaches on data from 1990 to 2014 in a STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework. Key empirical results indicate that these factors may contribute to environmental improvement in the short run; however, there are adverse implications in the long-run. Specifically, economic factors including economic growth, trade openness and foreign direct investment cause environmental degradation in the under-analysis economies. The sociological factors as measured by the population growth and the level of urbanization also show a negative impact on the environmental degradation in the short-run but in the long run, both population size and urbanization increase environmental degradation. These findings are in line with the concerns raised by Thomas Robert Malthus in his Essay on the Principle of Population. With regards to the energy factors, it indicates that the renewable energies help the European environment by reducing the level of carbon dioxide emissions whereas the higher energy intensity is an ecological threat. Our results remain robust in the EKC framework.

Energy innovations and environmental sustainability in the U.S.: The roles of immigration and economic expansion using a maximum likelihood method

Environmental degradation remains a huge obstacle to sustainable development. Research on the factors that promote or degrade the environment has been extensively conducted. However, one important variable that has conspicuously received very limited attention is energy innovations. To address this gap in the literature, this study investigated the effects of energy innovations on environmental quality in the U.S. for the period 1974 to 2016. We have incorporated GDP and immigration as additional regressors. Three indices comprising of CO₂ emissions, ecological footprint and carbon footprint were used to proxy environmental degradation. The cointegration tests established long-run relationships between the variables. Using a maximum likelihood approach with a break, the results showed evidence that energy innovations significantly improve environmental quality while GDP degrades the quality of the environment, and immigration has no significant effect on the environment. Policy implications of the results are discussed in the body of the manuscript.

Identify the significant contributors of regional CO2 emissions in the context of the operation of high-speed railway-illustrated by the case of Hunan Province

High-speed rail (HSR) is one of the essential innovations in the field of transportation in the latter half of the twentieth century. In China, the rapid development of HSR has received increasing attention and resulted in a boost of tourism, with significant impact on the development of cities that operates HSR. To accurately comprehend how will the operation of HSR influence the regional CO₂ emissions, this paper applies the modified STIRPAT model, combining with real data on high-speed rail passenger flow volume of the Beijing-Guangzhou high-speed rail Hunan section. The results show that (1) the high-speed rail operation is also a significant impact factor for regional CO₂ emissions. (2) Considering the operation of HSR, the ranking of contribution rate of driving factors for regional CO₂ emissions is as follows: GDP per capita, energy consumption per unit of GDP, arrival volume of high-speed rail, originated volume of high-speed rail, the proportion of coal in the energy mix, proportion of the tertiary industry, and population. (3) Surprisingly, the numerical research result shows that the operation of HSR for the cities may promote regional CO₂ emissions, while the increase in urban population and the optimization of energy structure have a reducing effect on regional carbon emissions. There is a transfer effect of the operation of HSR and region development, which results in the rising of regional CO₂ emissions. Thus, it is urgent to research on the decoupling of economic growth from CO₂ emissions. The findings could be conducive for the government and railway company to evaluate and administrate the operation of high-speed rail and adequately deal with the relationships between the high-speed railway and regional development.

Determinants of carbon emissions in Pakistan's transport sector

The transport infrastructure plays an imperative role in a country's progress. At the same time, it causes environmental degradation due to extensive use of fossil fuels. The transport system of Pakistan is largely dependent on nonrenewable energy sources (oil, coal, and gas), which are hazardous to environmental quality. This research uses an autoregressive distributive lag model (ARDL) to examine the impact of oil prices, energy intensity of road transport, economic growth, and population density on carbon dioxide (CO₂) emissions of Pakistan's transport sector during the 1971-2014 period. The ARDL bounding test examines the cointegration and long-run relationships among the variables, and the directions of causal relationships are found through the Granger causality vector error correction model (VECM). The long-run results indicate that increases in oil prices and economic growth help to reduce the transport sector's CO₂ emissions, while rising energy intensity, population concentration, and road infrastructure increase them, with population playing a dominant role. The findings of this study can help authorities in Pakistan to develop suitable energy policies for the transport sector. Among other recommendations, the study recommends investment in renewable energy projects and energy-efficient transport systems (e.g., light train, rapid transport system, and electric busses) and environmental taxes (subsidies) on the vehicles that use fossil fuels (renewable energy).

The effect of energy R&D expenditures on CO2 emission reduction: estimation of the STIRPAT model for OECD countries

Energy innovations are critical to combating global warming and climate change. In this context, we focus on the impact of energy research-development (R&D) expenditures, which are the input of energy innovations, on CO₂ emissions. For this purpose, we investigate the effect of disaggregated energy R&D expenditures on CO₂ emission in 19 high-income OECD countries over the period 2003-2015. The dynamic panel data method is followed for empirical analysis. The results of the study show that R&D expenditures for energy efficiency and fossil energy have an increasing effect on CO₂ emissions. Contrary to expectations, there is no significant relationship between renewable energy R&D expenditures and CO₂ emissions. Remarkably, there is strong evidence that the power and storage R&D expenditures have a reducing effect on CO₂ emissions. In light of the empirical findings, policy implications and recommendations to potential readers and authorities are further discussed.

Impact of energy consumption and human activities on carbon emissions in Pakistan: application of STIRPAT model

This study examines the impact of fossil fuel consumption, nonrenewable energy consumption, population, affluence, and poverty on carbon emissions in Pakistan by using a time series data from 1972 to 2014. The study uses a flexible ecological framework known as the STIRPAT model. The Auto Regressive Distributive Lag (ARDL) Model and Error Correction Model (ECM) are used to estimate the robust results. The results show that consumption of fossil fuels, population growth, improvement in affluence level, and urbanization are contributing factors to high carbon emissions in Pakistan. The results also highlight that poverty alleviation and carbon emissions have opposite trends, this shows that the efforts to reduce poverty are stimulating the consumption of low-cost energy sources such as fossil fuels, and contributing to carbon emissions. However, results indicate that an increase in the share of renewable energy in total energy use and consumption of hydroelectric energy has the potential to reduce carbon emissions in Pakistan. The results highlight that there is a need to promote the use of renewable and hydroelectric energy. At domestic level, this will assist to meet the energy demand of the growing population and also prove helpful to reduce carbon emissions. Thus, the study recommends that a transition from fossil fuel energy to renewable and hydroelectric energy could prove an effective strategy to improve the affluence level, to alleviate poverty and effective to reduce carbon emissions in Pakistan.

Are Small Cities More Environmentally Friendly? An Empirical Study from China

City sizes are rapidly expanding, and urban air pollution is a serious challenge in China. PM_2.5 (fine particulate matter) is the primary pollutant of urban pollution. This study aimed to examine the correlations between PM_2.5 and city size. In this paper, using the panel data of 278 cities in China from 2007 to 2016, we constructed a static and dynamic panel model based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) analytical framework. We found that there was a significantly inverted N-shaped correlation between PM_2.5 and city size. Two inflection points were found at 949,200 and 3,736,100. We found no evidence to support the EKC (Environmental Kuznets Curve) hypothesis, while the "Pollution Haven Hypothesis" gained support. The contradiction between PM_2.5 and city size will exist for the long term. Policy recommendations were proposed based on our findings. Controlling the city size does not seem to be necessary for very large cities as they have passed the second inflection point. Cities with a growing population are under great pressure to prevent PM_2.5 pollution and need to implement greater measures to reduce pollution.

Urbanization impact on residential energy consumption in China: the roles of income, urbanization level, and urban density

This paper investigated the impact of urbanization on residential energy consumption (REC) in China by taking cognizance of the levels of income, urbanization and urban density. Threshold analyses were employed to investigate the nonlinear relationships based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework using a balanced panel dataset of 29 provinces of China over the period of 1998-2014. The common correlated effects mean group estimator (CCEMG) was used to address time-series cross-section (TSCS) issues. The results confirmed the existence of the nonlinear relationship between urbanization and REC in China. The impact of urbanization on REC varied at different economic development levels and urbanization levels. Specifically, urbanization decreased REC at the stage that per capita disposable income of urban residents (PDI) less than 2615 USD, while it increased REC at the stage that PDI higher than 2615 USD. Similarly, urbanization decreased REC at the stage that urbanization rate lower than 55.31% and increased REC after urbanization rate exceeded 55.31%. This study did not find evidence to support the urban environmental transition theory, indicating there was still no region in China had stepped into the win-win stage of urbanization and energy consumption. Furthermore, the nonlinear impact of urban density on REC was estimated and the results indicated that urban density exerted a positive effect on REC when urban density was lower than 808 inhabitants per square kilometer, while it was no longer relevant to REC after that threshold point. Based on these results, the corresponding countermeasures and suggestions to achieve low-carbon urbanization were put forward.

The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis

While the progress of China’s industrialization and urbanization has made great strides, atmospheric pollution has become the norm, with a wide range of influence and difficult governance. While many previous works on NOx pollution have been developed from the perspectives of natural science and technology, few studies have been conducted from social-economic points of view, and regional differences have not been given adequate attention in driving force models. This paper adopts China’s provincial panel data from 2006 to 2015, an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model, and spatial econometric models to investigate the socio-economic influential factors and spatial-temporal patterns of NOx emissions. According to the spatial correlation analysis results, the provincial NOx emission changes not only affected the provinces themselves, but also neighboring regions. Spatial econometric analysis shows that the spatial effect largely contributes to NOx emissions. The other explanatory variables all have positive impacts on NOx emissions, except for the vehicular indicator (which did not pass the significance test). As shown through the estimated consequences of direct and indirect effects, the indicators have significant positive effects on their own areas, and exacerbate NOx pollution. In terms of indirect effects, only three factors passed the significant test. An increase in gross domestic product (GDP) and energy consumption will exacerbate adjacent NOx pollution. Finally, a series of socio-economic measures and regional cooperation policies should be applied to improve the current air environment in China.

An integrated specification for the nexus of water pollution and economic growth in China: Panel cointegration, long-run causality and environmental Kuznets curve

This paper concentrates on a Chinese context and makes efforts to develop an integrated process to explicitly elucidate the relationship between economic growth and water pollution discharge-chemical oxygen demand (COD) discharge and ammonia nitrogen (NH₃-N), using two unbalanced panel data sets covering the period separately from 1990 to 2014, and 2001 to 2014. In our present study, the panel unit root tests, cointegration tests, and Granger causality tests allowing for cross-sectional dependence, nonstationary, and heterogeneity are conducted to examine the causal effects of economic growth on COD/NH₃-N discharge. Further, we simultaneously apply semi-parametric fixed effects estimation and parametric fixed effects estimation to investigate environmental Kuznets curve relationship for COD/NH₃-N discharge. Our empirical results show a long-term bidirectional causality between economic growth and COD/NH₃-N discharge in China. Within the Stochastic Impacts by Regression on Population, Affluence and Technology framework, we find evidence in support of an inverted U-shaped curved link between economic growth and COD/NH₃-N discharge. To the best of our knowledge, there have not been any efforts made in investigating the nexus of economic growth and water pollution in such an integrated manner. Therefore, this study takes a fresh look on this topic.

The environmental impact of obesity: longitudinal evidence from the United States

OBJECTIVE

This paper examines the relationship between obesity and greenhouse gas (GHG) emissions while accounting for the environmental impact of growth in transportation output and in crop and animal farming.

STUDY DESIGN

The study makes use of US state-level longitudinal data over the 1997-2011 period.

METHODS

Random effects and fixed effects estimators are employed within a multiple regression analysis framework.

RESULTS

After controlling for other sources of emissions, there is evidence that the effect of transportation output on CO₂ emissions worsens at obesity rates exceeding 33.7% and the effect on N₂O emissions worsens at obesity rates exceeding 22.5%. In addition, the impact of crop and animal farming on N₂O emissions worsens at obesity rates exceeding 20.2%.

CONCLUSION

This paper provides significant and new insight about the causal link between obesity and environmental emissions and highlights the importance of addressing the obesity epidemic on public health and environmental grounds. Thus, mitigating GHG emissions connected to obesity requires joint effort between policymakers, public health officials, and parties from concerned economic sectors in pursuing remedial actions to reverse the current obesity trend. Various policy measures are discussed.

Political influences on greenhouse gas emissions from US states

Starting at least in the 1970s, empirical work suggested that demographic (population) and economic (affluence) forces are the key drivers of anthropogenic stress on the environment. We evaluate the extent to which politics attenuates the effects of economic and demographic factors on environmental outcomes by examining variation in CO2 emissions across US states and within states over time. We find that demographic and economic forces can in part be offset by politics supportive of the environment--increases in emissions over time are lower in states that elect legislators with strong environmental records.