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

Spatial-temporal differentiation and influencing factors of carbon emission trajectory in Chinese cities - A case study of 247 prefecture-level cities

Cities, where human energy activities and greenhouse gas emissions are concentrated, contribute significantly to alleviating the impacts of global climate change. Utilizing the China Carbon Emissions Accounting Database (CEADs) to provide carbon dioxide emission inventories for urban areas in China at the prefecture level, this study closely examines the historical evolution trajectories of carbon emissions across 247 urban units from 2005 to 2019. The logarithmic cubic function model was employed to simulate these trajectories, evaluating urban emission peaks and classifying the different carbon emission trajectories. Further, the Geographical and Temporal Weighted Regression model was employed to explore spatiotemporal traits and essential variables that impact the variations in carbon emissions among four identified trajectory types. Our results showed that Chinese urban carbon emission trajectories can be classified into four categories: a) peaking emissions, b) fluctuating growth, c) continuous growth, and d) passive decline. Specifically, 43 cities, primarily in North China, proactively attained their emission peak post-2010, driven by the reduction in secondary industry and energy intensity. 90 cities, largely industrial hubs in the southeast coast and inland, reached an emission plateau around 2015, exhibiting fluctuating growth due to dependencies on secondary industries. 101 cities, predominantly located in western and central regions, demonstrated a clear upward trend in carbon emissions, propelled by rapid urbanization and heavy industry-oriented economic development. Lastly, 13 cities, typically in the northeastern and southwestern regions, experienced a passive decline in carbon emissions, attributable to resource depletion or economic downturns. It is evident that China's city-level carbon peaking has demonstrated some effectiveness, yet considerable progress is still required.

A dual-core system dynamics approach for carbon emission spillover effects analysis and cross-regional policy simulation

As carbon emission continue to rise and climate issues grow increasingly severe, countries worldwide have taken measures to reduce carbon emission. However, carbon dioxide is continuously flowing in the atmosphere and is easily influenced by neighboring cities' policies. Therefore, how to solve the problem of carbon emission spillover effect has become the key to improve policy efficiency. Cross-regional carbon governance provides a perspective on solving the carbon emission problem by regulating and guiding the cooperative behavior of cross-regional governance actors. Taking Chengdu-Chongqing area as an example, this study used the SDM to analyze the influencing factors and spatial spillover effects of emission. Then we used the system dynamics method to construct a dual-core carbon emission system, and simulated the spillover effect and emission reduction potential of Chengdu and Chongqing emission reduction policies under different policy schemes. The results reveal that the mobility of population and enterprises have a significant impact on carbon emission prediction. Carbon reduction policies exhibit the phenomena of "carbon transfer" and "free-riding." When Chengdu lowers its economic growth rate, it leads to the transfer of high energy-consuming enterprises to Chongqing, increasing carbon emission in Chongqing. The implementation of comprehensive carbon reduction policies in Chongqing has a positive effect on Chengdu. Emission reduction policies exhibit issues related to their temporal efficacy, as the effects of industrial structural policies in Chengdu yield opposite outcomes in the short and long term. Each city's unique circumstances necessitate tailored carbon reduction policies. In order to reduce carbon emissions, Chengdu and Chongqing require opposite population policies.

Dynamic simulation of carbon emission under different policy scenarios in Pearl River Delta urban agglomeration, China

Global climate continues to warm; by reducing carbon emission (CE) to cope with climate warming has become a global consensus. The influencing factors of CE exhibit diversification and spatial characteristics, and the complexity of the CE system poses challenges to green and low-carbon development and the realization of China's dual-carbon goals. Taking the Pearl River Delta urban agglomeration as an example, this study explored the influencing factors of CE and designed emission reduction schemes with the help of multi-scale geographically weighted regression (MGWR). Based on this, the system dynamics model was used to construct a CE system framework considering multi-dimensional driving factors, so as to combine the complex CE system with the emission reduction countermeasures considering spatial heterogeneity, and realize the dynamic simulation of CE reduction policies. The results showed that the urban agglomeration as a whole will reach carbon peak by 2025. Shenzhen, Zhuhai, and Dongguan have achieved carbon peak before 2020, while other cities will reach carbon peak by 2025-2030. The government policy constraints can effectively curb CE, but if government constraints were relaxed, CE will rise and individual cities will not reach carbon peak. Comprehensive CE reduction policies are better than a single CE reduction policy. The study found that this model framework provides a systematic analysis of carbon reduction strategies for urban agglomerations, offering decision-makers various combinations of economic development and green low-carbon objectives. This will further contribute to a multi-faceted mitigation of high emission in urban agglomeration and promote regional sustainable development.

Emission accounting and drivers in Central Asian countries

Emerging countries are at the frontier of climate change actions, and carbon emissions accounting provides a quantifiable measure of the environmental impact of economic activities, which allows for comparisons of emissions across different entities. However, currently there is no study covering detailed emissions inventories for emerging countries in Central Asian. This paper compiles detailed and accurate carbon emissions inventories in several Central Asian countries (i.e., Kazakhstan, Kyrgyzstan, Pakistan, Palestine, Tajikistan, and Uzbekistan) during the period 2010-2020. Using the IPCC administrative territorial approach, we for the first time compile their emissions inventories in 47 economic sectors and five energy categories. Moreover, we also investigate decoupling status based on Tapio decoupling model and examine emissions driving factors based on the index decomposition analysis method. The primary results illustrate that carbon emissions in Central Asian countries are increasing with huge differences. Decoupling results highlight that most of the sample countries still need more effort to decouple the economy and emissions except that Pakistan achieves an ideal strong decoupling state. The results of the decomposition indicate that the economy and population both raise emissions, while energy intensity and carbon intensity are negative drivers in some countries. We propose practical policy implications for decarbonization and energy transition roadmap in Central Asian countries.

Climate change mitigation role of renewable energy consumption: Does institutional quality matter in the case of reducing Africa's carbon dioxide emissions?

Renewable energy and institutions have emerged among other variables touted to address climate change problems. However, empirical results have been conflicting. With a relatively poorer state of institutional quality and a lower level of renewable energy development amidst rising carbon dioxide (CO₂) emissions in Africa, the study assesses: a) the direct effect of renewable energy and institutional quality on CO₂ emissions in Africa; and b) the moderation role of institutional quality on the effect of renewable energy on CO₂ emissions in Africa. The study relies on panel data covering 2002-2021 for 32 African countries. The Fully-Modified OLS regression method is employed to analyze the data based on the environmental Kuznets curve (EKC) hypothesis and Stochastic Impacts by Regression on Population, Affluence, & Technology (STIRPAT) model. The results show that urbanization and trade openness increase CO₂ emissions. Although income has a positive effect on carbon emissions, the square term has a negative confirming the EKC hypothesis. Renewable energy also reduces CO₂ emissions. Institutional quality variables of control of corruption, rule of law, regulatory quality, political stability and absence of violence, voice and accountability, government effectiveness and institutional index created from the above indicators reduce CO₂ emissions. In addition, except government effectiveness, the remaining indicators of institutional quality negatively moderate the effect of renewable energy on CO₂ emissions. The results among other things imply that intensifying the development and usage of renewable energy would help address the rising carbon dioxide emissions trend in Africa. Also, strengthening institutions promises to reduce CO₂ emissions.

Impacts of financial development on the energy consumption in China from the perspective of poverty alleviation efficiency

Poverty alleviation and energy saving are two major issues of sustainable development targets. Meanwhile, financial development (FD) is a powerful engine of economic growth, which is regard as a valid approach to contain the demand for energy consumption (EC). However, few studies link the three factors and explore the specific impact mechanism of poverty alleviation efficiency (PE) on the tie between FD and EC. Thus, we employ the mediation and threshold models to evaluate the influence of FD on the EC in China during 2010-2019 from the perspective of PE. We affirm that FD indirectly promotes EC through the channel of PE. The mediating effect of PE is responsible for 15.75% of the total effect of FD on the EC. Moreover, FD generates a significant threshold impact on the EC considering the change of PE. When the PE exceeds 0.524, the role of FD in promoting EC is strengthened. Ultimately, the outcome suggests policymakers need to prominent the trade-off between energy saving and poverty reduction during the fast evolution of financial system.

Does environmental degradation matter for poverty? Clarifying the nexus between FDI, environmental degradation, renewable energy, education, and poverty in Morocco and Tunisia

Poverty is the curse for sustainable and equitable development worldwide by detreating environmental sustainability, economic instability, and inequality. However, as a remedy for poverty reduction, researchers over the past decade have examined the key macro determinants and established positive associations, implying the contributory role in poverty reduction. The study explores the environmental, energy, education, and foreign direct investment (FDI) effects on poverty reduction in Morocco and Tunisia from 1991 to 2020. We employed autoregressive distributed lagged (ARDL) and nonlinear ARDL frameworks to document the explanatory variables' elasticity on poverty reduction in both the long- and short-run horizons. According to linear assessment, the study documented that education, energy, and FDI support poverty reduction. At the same time, the cost of environmental degradation has a detrimental effect on poverty augmentation. Referring to asymmetric assessment, the study established a long-run asymmetric association between asymmetric shocks of education, FDI, and energy with poverty. For directional association, the study has implemented a casualty test with the Fourier TY causality test and revealed a bidirectional association between education and poverty, and energy and poverty. Moreover, the unidirectional casualty was unveiled between FDI and poverty, and poverty and environment.

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.

The Relationship between Spatial Characteristics of Urban-Rural Settlements and Carbon Emissions in Guangdong Province

As containers of human activities, both urban and rural built-up settlements play roles in the increment of regional GHG emissions. This study investigates the relationship between the spatial characteristics of different urban-rural settlements and carbon emissions in Guangdong province, China. After estimating the carbon emissions of 21 cities in Guangdong province from 2005 to 2020, this paper constructs a panel regression model based on the STIPRAT model to identify the impact of different types of urban-rural settlements on carbon emissions with controlling socioeconomic factors. The results show that the increase in high-density urban areas and low-density rural built-up areas have a significant positive correlation with carbon emissions. Moreover, the impact of rural built-up settlements is stronger than urban areas. In addition, our results indicate that carbon emission has little correlation with the spatial landscape pattern. This study highlights the importance of rural built-up settlements for understanding regional carbon emissions. Local governments should not only focus on the reduction of carbon emissions in the large urban agglomerations but also need to make a plan for the small and medium-sized towns that are dominated by industries.

Exploring the dynamic nexus between renewable energy, poverty alleviation, and environmental pollution: fresh evidence from E-9 countries

The present study aims to scrutinize the long- and short-run relationship along with the direction of causality among environmental pollution (CO₂), renewable, non-renewable energy, income disparity, exchange rate, and poverty alleviation in E-9 countries of continent Asia, using a panel dataset from 1990 to 2018. The current study used pooled mean group autoregressive distributed lag (PMG ARDL) and Dumitrescu-Hurlin (D-H) causality test after affirming a stable long-run association among environmental pollution and all the explanatory variables. However, ECM (error correction mechanism) was specified to explore short-run dynamics. The study's outcomes confirmed strong co-integration among environmental pollution (CO₂), renewable, non-renewable energy, income disparity, exchange rate, and poverty alleviation. Moreover, uni (bi) directional causality runs from non-renewable energy, exchange rate, and income disparity (poverty alleviation and renewable energy) to environmental pollution (CO₂). Results also revealed that poverty alleviation, exchange rate, and renewable energy usage substantially negatively influence environmental pollution (CO₂). Contrarily, income disparities and non-renewable energy usage positively influence long- and short-run environmental pollution. Therefore, from the policy perspective, the current study focused on twofold; first, there is a desire to alleviate poverty, the decline in non-renewable energy use and income disparity among upper and lower-income quintiles. Second, boost exchange rate and renewable energy use to control environmental pollution in the described least developed countries (LDCs).

Energy mix with technological innovation to abate carbon emission: fresh evidence from Mexico applying wavelet tools and spectral causality

The global warming issue arises from climate change, which draws scientists' attention toward cleaner energy sources. Among clean sources, renewables and nuclear energy are getting immense attention among policymakers. However, the significance of nuclear energy in reducing CO₂ emissions has remained ambiguous, necessitating further research. Therefore, the present study draws impetuous attention to the United Nations Sustainable Development Goals-7 (affordable clean energy) & 13 (climate change mitigation) by looking at the relationship between energy mix (fossil fuels, renewables, and nuclear), economic growth, technological innovation, and CO₂ emissions in Mexico from 1980 to 2019 using the autoregressive distributed lag (ARDL) model. In addition, to assess the direction of causality, this study applied wavelet techniques and spectral causality. The findings affirm that renewable and nuclear energy use and technological innovation tend to curb CO₂ emissions, whereas fossil fuel consumption and economic expansion trigger CO₂ emissions. The study lends support to the environmental Kuznets curve (EKC) phenomenon in Mexico. The FMOLS and DOLS tests show that our long-run estimates are reliable. In different time scales, the wavelet coherence result is also consistent. Finally, the results of the spectral causality approach demonstrate a significant causal association between the variables tested at various frequencies. As a result, in order to achieve SDGs 7 and 13 and support an environmentally friendly ecosystem, Mexico's energy mix must be changed to renewables and nuclear.

Industrialization, energy consumption, and environmental pollution: evidence from South Asia

This study has been carried out to analyze the contribution of industrialization and energy consumption by keeping the role of urbanization on environmental pollution for South Asia. The study used Augmented Mean Group (AMG), Common Correlated Effects Mean Group (CCEMG) analysis, Westerlund co-integration test, and Dumitrescu-Hurlin causality test for the panel of South Asia covering the period 1984-2016. To measure the status of environmental pollution, CO₂ is taken as a proxy indicator, and industrialization is measured by the industrial value-added. The results of AMG demonstrate that industrialization and energy consumption are significant indicators of environmental pollution and these empirical findings are also confirmed by the CCEMG model. The long-run co-integration between industrialization, energy use, urbanization, capital, and environmental pollution is also confirmed by the Westerlund co-integration test. The findings of the Dumitrescu-Hurlin causality test also confirmed the bidirectional causality between industrialization and pollution. A unidirectional causality is observed from energy consumption to pollution. The study suggests formulating policies for energy-efficient technologies in the industrial sector and the high-speed pattern of urbanization.

Are globalization, urbanization, and energy consumption cause carbon emissions in SAARC region? New evidence from CS-ARDL approach

This study examines the impact of energy consumption, urbanization, and globalization on environmental degradation proxied by carbon emissions (CO₂) in the South Asian Association for Regional Cooperation (SAARC) countries, namely Sri Lanka, Pakistan, Maldives, Nepal, Bhutan, Bangladesh, and India using data over the period 1990-2018. The cross-sectional autoregressive distributed lag (CS-ARDL), pooled mean group (PMG), and Dumitrescu and Hurlin (D-H) Granger causality techniques are employed for the empirical analysis. First and second-generation panel unit root tests are used to determine the stationary level of all data series which reveals mixed order of integration. The empirical findings show that urbanization, gross domestic product (GDP) per capita income, energy consumption, industrial growth, globalization, and financial development cause CO₂ emissions, while the other variables, namely arable land and innovation, put negative effects on CO₂ emissions. Moreover, the D-H heterogeneous test results exhibit that bi-directional relationship exists between CO₂ and arable land, urbanization, industrial growth, and financial development, while a unidirectional causality exists between CO₂ emissions and GDP per head income. These findings suggest that planned urbanization, investment in renewable energy sources, and effective strategies regarding the economic and financial integration with the global economies are required for a clean and green environment.

An empirical study on correlation among poverty, inclusive finance, and CO2 emissions in China

This paper explores the nonlinear relationship between poverty and CO₂ emissions based on the panel data of 30 provinces in China from 2005 to 2019. In this study, the autoregressive distributed lag (ARDL) model is first used. Findings confirm that poverty has a negative impact on CO₂ emissions in the short run and a positive impact in the long run, while both effects of inclusive finance on CO₂ emissions are negative. In order to explore the reasons for the change in the coefficient of poverty, we introduce a moderating effect (ME) model and a dynamic panel threshold (DPT) model. The result shows that the negative effect of poverty on CO₂ emissions diminishes with the moderation of inclusive finance. When inclusive finance crosses the threshold value (IFI = 0.2696), the impact of poverty on CO₂ emissions will change from negative to positive gradually, which verifies the applicability of the "Poverty-CO₂ Paradox" in China and provides an empirical basis for breaking the "Poverty-CO₂ Paradox." Consequently, deepening poverty reduction and pushing the region's inclusive finance to the threshold level are proposed as effective ways to promote CO₂ emission reduction.

Assessing the Role of Green Finance and Education as New Determinants to Mitigate Energy Poverty

Energy poverty (EP) is a problem that affects developed and developing economies, and its mitigation is of great significance to social welfare. EP affects Latin American countries, and policymakers have recently attempted to address this issue, particularly in the aftermath of the recent economic crisis. It is essential to measure and evaluate EP to implement strategies and policies effectively. Using a panel quantile regression approach, we investigate the heterogeneous impact of green finance, renewable energy (RE), and energy efficiency (EE) on EP for 33 Latin American countries from 2000 to 2018. Furthermore, certain associated control variables are incorporated into our model to avoid an omitted variable bias. According to empirical results, the impact of independent variables on EP is heterogeneous. Specifically, green finance is an essential source of alleviating EP, and it has a significant positive effect across all quantiles, but it is especially strong in the middle quantiles. RE and EE significantly mitigate EP, with the strongest effects occurring at higher quantiles. By including green finance, RE, and EE as the main explanatory determinants of EP, the findings urge policymakers in Latin American countries to design a comprehensive energy conservation policy to minimize the effects of massive EP.

Tourism-induced emission in Sub-Saharan Africa: A Panel Study for Oil-Producing and Non-oil-Producing countries

The tourism industry is undoubtedly among the largest contributors to economic growth and employment generation in most economies of the world, and Africa is not an exception as outlined by World Tourism Organization (UNWTO). Thus, many countries in sub-Saharan Africa (SSA) are paying more attention to tourism development as alternative growth path to boost their economies. However, the tourism-induced growth is not void of its environmental issues. To this end, this study using recent econometrics analysis explored the nexus between tourism arrival GDP growth, urbanization, carbon dioxide emission, and foreign direct investment for oil and non-oil sub-Saharan Africa (SSA) countries, that is, to ascertain the real impacts of tourism and FDI on the environmental performance of the regions. Empirical results show that tourism, GDP growth, and FDI dampen the quality of the environment. For instance, a 1% increase in tourism activities worsens the quality of the environment by 1.09%. Interestingly, renewable energy shows statistical strength to improve environmental quality. The causality analysis resonates with the outcomes of the regression by giving credence to one-way causality between tourism and carbon dioxide emission. A similar trend of causality is seen between FDI and carbon dioxide emission and urbanization and carbon dioxide emission. Thus, as a policy prescription, strict environmental guidelines and regulations are necessary for controlling the unhealthy and undue economic activities that are suspected to impact environment negatively.

Relationship between Household Dynamics, Biomass Consumption, and Carbon Emissions in Pakistan

Over the years, the household sector has become an important energy consumer and the main source of greenhouse gas (GHG) emissions. The rural household sector has significant potential for emission reduction due to its heavy reliance on traditional fuels and technologies. A great number of academic studies have been undertaken to analyze patterns of household energy and their determinants around the globe, particularly in developing countries. However, little is known about the association between household dynamics and patterns of energy (biomass vs. non-renewable) use. This study aims to analyze the relationship between different household dynamics, such as household size, income, climate, availability of resources, markets, awareness, consumption of energy, and carbon emissions. The study uses the STIRPAT model to investigate the impact of income, household size, housing dimensions, clean energy, and market accessibility on energy consumption. The findings of the study reveal that biomass energy accounts for the majority of household energy consumption and dung has the highest share in total household energy consumption (39.11%) The consumption of biomass increased with the size of the household and decreased with the level of income. A 1 kgoe increase in biomass consumption resulted in a 15.355 kg increase in CO2 emissions; on the other hand, a 1 kgoe increase in non-renewable-energy consumption resulted in just a 0.8675 kg increase in CO2 emissions. The coefficients of housing unit size, distance from the LPG market, and livestock were the primary determinants for choosing any fuel. Having knowledge of modern cookstoves, clean energy, and the environmental impact of fuels reduced the consumption of both energy sources. Furthermore, it was found that households with a greater reliance on biomass emitted higher quantities of carbon compared to those with a low reliance on biomass. Based on the results of the study, it can be stated that a reduction in the use of biomass and non-renewable energy is possible with adequate interventions and knowledge.

Sustainable development and pollution: the effects of CO2 emission on population growth, food production, economic development, and energy consumption in Pakistan

Population growth has been a leading driver of global CO2 emissions over the last several decades. CO2 emission and greenhouse gas emissions are a key issue in the world that affects food production and also causes the climate change. The core purpose of this study was to inspect the influence of carbon dioxide emission to population growth, food production, economic growth, livestock and energy utilization in Pakistan. The STIRPAT (Stochastic Impact by Regression on Population, Affluence and Technology) model with the extension of an ARDL (Autoregressive Distributed Lag) method was utilized to demonstrate the linkage amid variables. Outcomes during short-run investigation reveal that variables population growth, economic growth, rural population growth, livestock production uncovered a productive association with CO2 emission. Furthermore, via long-run population growth, economic growth, rural population growth, livestock production and energy utilization have positive interaction with CO2 emission, while the variables food production and urban population growth demonstrated an adverse influence to CO2 emission during long- and short-run interaction. Similarly, the error correction model exposed that population growth, economic progress, livestock and energy utilization have constructive interaction to CO2 emission, while the variables food production, urban and rural population growth exposed an adverse impact to CO2 emission. On the basis of this analysis, we will address the strategic consequences.

Evaluating an economic application of renewable generated hydrogen: A way forward for green economic performance and policy measures

Energy security and environmental measurements are incomplete without renewable energy; therefore, there is a dire need to explore new energy sources. Hence, this study aimed to measure the wind power potential to generate renewable hydrogen (H2), including its production and supply cost. This study used first-order engineering model and net present value to measure the levelized cost of wind-generated renewable hydrogen by using the data source of the Pakistan Meteorological Department and State Bank of Pakistan. Results showed that the use of surplus wind and renewable hydrogen energy for green economic production is suggested as an innovative project option for large-scale hydrogen use. The key annual running expenses for hydrogen are electricity and storage costs, which have a significant impact on the costs of renewable hydrogen. The results also indicated that the project can potentially cut carbon dioxide (CO2) pollution by 139 million metric tons and raise revenue for wind power plants by US$2998.52 million. The renewable electrolyzer plants avoided CO2 at a rate of US$24.9-36.9/ton under baseload service, relative to US$44.3/ton for the benchmark. However, in the more practical mid-load situation, these plants have significant benefits. Further, the wind-generated renewable hydrogen delivers 6-11% larger annual rate of return than the standard CO2 catch plant due to their capacity to remain running and supply hydrogen to the consumer through periods of plentiful wind and heat. Also, the measured levelized output cost of hydrogen (LCOH) was US$6.22/kgH2, and for the PEC system, it was US$8.43/kgH2. Finally, it is a mutually agreed consensus among environmental scientists that the integration of renewable energy is the way forward to increase energy security and environmental performance by ensuring uninterrupted clean and green energy. This application has the potential to address Pakistan's urgent issues of large-scale surplus wind- and solar-generated energy, as well as rising energy demand.

Applying a dynamic ARDL approach to the Environmental Phillips Curve (EPC) hypothesis amid monetary, fiscal, and trade policy uncertainty in the USA

It is well known that unemployment and environmental degradation are two critical issues across the globe. However, there is an extended dearth of literature that explores the nexus between unemployment and environmental degradation. Kashem and Rahman (Environ. Sci. Pollut. Res. 27(101): 31153-31170, 2020) put forward the Environmental Phillips Curve (EPC) hypothesis, which depicts a negative relationship between unemployment and environmental degradation. This study further explores the validity of the EPC hypothesis in the case of the USA. It also investigates the impact of monetary policy uncertainty (MU), fiscal policy uncertainty (FU), and trade policy uncertainty (TU) on carbon dioxide emissions. To this end, the analysis employs the novel methodology of the dynamic ARDL model. The results document that EPC does not hold in the short run, but it does in the long run. Furthermore, both in the short and long run, MU escalates CO2 emissions, while FU plunges emissions in both the short and long run. Finally, TU does not alter the level of CO2 emissions.

How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries

This study measures the environmental regulation effect and pattern of carbon emission and energy efficiency through data envelopment analysis and econometric estimation. One of the most important ways to achieve a green transition is promoting technical progress through environmental regulation. Though China has witnessed rapid economic growth over the last two decades, the country can improve it further through adopting sustainable green energy and establishing more energy-efficient industries to strike a good balance between economic and social developments. The oil and carbon dioxide emission performances form the most important metrics. This study uses panel data from 30 Chinese provinces from 2008 to 2017 to assess the effect of environmental regulation on energy production. The nonradial directional distance function (NDDF) is used to measure the total factor energy efficiency index (TFEEI). The panel system GMM model, which can effectively address endogenous problems and regional variability, is utilized to research the nonlinear relationship between environmental regulations and EEI under various environmental regulations to study it. The findings reveal a considerably modest total average EEI amount for energy-intensive industries, averaging between 0.55 and 0.58, which is way below the ideal value (i.e., 1). Furthermore, the results of the dynamic panel data model revealed a significant U-shaped relationship between China's EEI and environmental regulation. The results show that as the values of market-based environmental regulations (MERs) and command and control environmental regulations (CCERs) exceed the corresponding levels, the impact of environmental regulation on the TFEEI increases gradually. This study will aid policymakers in better understanding the efficacy of different levels of environmental regulations to make more educated decisions.

Accounting for the combined impacts of natural resources rent, income level, and energy consumption on environmental quality of G7 economies: a panel quantile regression approach

As the argument widens on the need to cut down on global carbon emissions, this study addresses environmental degradation using a combination of second-generation empirical methodologies including, quantile regression (QR), augmented mean group (AMG), fully modified ordinal least square (FMOLS), and dynamic ordinal least square (DOLS) to examine the impacts of natural resource rents alongside disaggregated energy consumption on the environmental quality of the G7 economies within the framework of the stochastic impact by regression on population, affluence, and technology (STIRPAT) model. The empirical findings reveal that the total natural resources rent indicates a positive significant relationship with pollution in all the quantiles except Q 0.05. Additionally, the findings for renewable energy consumption are adverse and significant throughout the assessed quantiles while fossil fuel energy consumption is reported to have a positive and significant effect on carbon dioxide emissions, thus, increasing environmental degradation experienced in the G7 economies. The extended findings from the Granger causality analysis also show that income levels combined with fossil fuel use have a strong effect on environmental degradation, while the total natural resources rent granger causes clean energy consumption within the G7 countries. This finding supports the assertions that natural resource revenue is mostly channeled into further productivity avenues which consequently lead to further environmental degradation. As such, while maintaining targeted revenue agenda, we strongly recommend that productivity gains from natural resource rents within the G7 economies should be harnessed for investment in clean energy for a more sustainable environment.

Fossil fuels consumption and carbon dioxide emissions in G7 countries: Empirical evidence from ARDL bounds testing approach

This research determines the intertemporal relationships caused by the coal, oil, and natural gas consumption in the carbon dioxide emission by the G7 countries from 1965 to 2018. Auto-regressive and Distributed Lags models and Bound test were used to detect cointegration and understand the dynamic effect. Due to structural breaks occurred in the variables, two dummy variables for the periods of breaks, 1978 and 1990 were incorporated respectively. Positive causality was identified, in the sense that the consumption of fossil fuels provides an increase in carbon dioxide emissions. Short-term elasticities indicate that an increase of 1 percentage point in the consumption of oil, coal, and natural gas will cause, respectively, an increase of 0.4823%, 0.3140%, and 0.1717% in carbon dioxide emissions. In the long run, the increase of 1 percentage point in the consumption of oil, coal, and natural gas will cause, respectively, an increase of 0.4924%, 0.2692%, and 0.1829% in carbon dioxide emissions. The error correction model (ECM = -0.4739) indicates that 47.39% of a shock in the carbon dioxide emissions variable is resolved in one year and after 2 years, carbon dioxide emissions return to long term equilibrium.

Testing role of green financing on climate change mitigation: Evidences from G7 and E7 countries

The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of the study are robust with VECM technique. The outcomes of the study revealed that climate change mitigation indicators significantly affect the GDP of G7 countries than that of E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques helps to clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, study results shown that a 1% rise in green financing index improves the environmental quality by 0.375% in G7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.The study also provides different policy implications for the stakeholders guiding to actively promote financial hedging for green financing. So that climate change and envoirnmental pollution reduction could be achieved effectively. The novelty of the study lies in study framework.

Industrial growth, clean energy generation, and pollution: evidence from top ten industrial countries

Unlike the previous study, this paper employs panel cointegration and Granger causation approaches to discuss the associations among carbon dioxide (CO₂) emissions, GDP growth, clean energy generation, and industrial growth for the top ten industrial countries spanning the period 1980-2014. The primary empirical outcomes show a two-way long-run association between environmental indicator, GDP growth, and clean energy generation, while one short-run causation from clean energy generation to CO₂ emissions and from industrial growth to clean energy generation. The computed coefficients elasticity's under FMOLS, DOLS, and CCR estimates revealed that the clean energy generation statistically contributes to declining emissions of CO₂ in Australia, Austria, and Chile while statistically increase emissions of CO₂ in Denmark and the Netherlands. Industrial growth statistically contributes to reducing emissions of CO₂ in Denmark and Norway but increases emissions in Chile, France, and Sweden. For the global panel, industrial growth leads to mitigate the rate of emissions while clean energy generation raises CO₂ emissions in the long period. Investing in clean energy is needed to stimulate the growth of the industrial sector and then reduce the rate of emissions.

Unveiling the asymmetric impact of energy consumption on environmental mitigation in the manufacturing sector of Pakistan

The manufacturing sector is the backbone for the development of an economy. Numerous studies investigated the impact of aggregative energy consumption on environmental degradation by using typical econometric techniques. To correct this gap, our study uses energy consumption and environmental degradation only in the manufacturing sector of Pakistan for the period 1985 to 2018. Our study also demonstrates the symmetric and asymmetric behaviour of energy consumption with carbon emissions by using a recently developed methodology by Shin et al. (2014). The findings of linear autoregressive distributive lag model shows that energy consumption and financial development intensify environmental degradation, while foreign direct investment and globalization mitigate environmental degradation that leads to validate pollution halo hypotheses in Pakistan. However, non-linear autoregressive distributive lag results confirm the asymmetric behaviour of energy consumption with co₂ emission. This study recommends the policies for policymakers in Pakistan to consider asymmetric behaviour of energy consumption as well as the installation of renewable energy sources and technological improvements in the industrial sector needed to enhance environmental sustainability. Further, there is a need to enhance globalization and foreign direct investment for Pakistan to achieve its environmental targets.

Population dynamics and environmental quality in Africa

The nexus of population dynamics and environmental degradation has been discussed widely in the extant literature. Most related studies have utilized carbon emission as a proxy of environmental quality. However, carbon emission does not capture the multidimensional nature of environmental degradation. To fill this gap, this study utilized the ecological footprint to capture environmental degradation because it is a more dynamic environmental quality measure. The paper examines the population-environmental degradation hypothesis for five populous African countries (DR Congo, Ethiopia, Nigeria, South Africa and Tanzania) using panel information from 1990 to 2019. The Cross-sectionally Augmented autoregressive distributed lag (CS-ARDL) was employed to assess the relationship among the data - ecological footprint per capita (ECFP), population growth rate (POPG), population density (POPD), urban population growth rate (URBN), age structure of the population (AGES), per capita GDP growth rate (PGDP), energy consumption (ENEC), and trade openness (TRAD). The findings of the study revealed that POPG, POPD, AGES, PGDP, ENEC and TRAD increase environmental degradation. Urbanization (URBN) has no significant influence on environmental degradation in the selected African countries. The study concludes with policy prescriptions geared towards addressing population expansion and improving environmental quality.

Nexus between energy policy and environmental performance in China: The moderating role of green finance adopted firms

This study measures the association between resources and the atmosphere; social and environmental aspects of energy production have become critical. In this context, the aim of this research is to explore the mediating effect of renewable energy patents in developing potential frameworks for energy policy viewpoints on the climate. The study took panel data from 2010 to 2017 and used a non-radial data envelopment analysis (DEA) process and panel data model for 30 Chinese provinces. The findings indicate that between 2010 and 2017, the average environmental efficiency index (EPI) of Chinese areas increased by 9.88%. When firms' internal variables are proxied by their commodity (revenue), the relationship term's point approximate coefficient is about 0.05. This magnitude means that a 1% rise in a company's assets will result in a 5% increase is estimated to be about 0.157, implying that a 1% rise in firm leverage is correlated with a 15.7%. Finally, based on the study results, some policy implications were proposed.

Evaluating green innovation and performance of financial development: mediating concerns of environmental regulation

This research measures the relationship between green innovation and the performance of financial development by using an econometric estimation during the year of 2000 to 2018 in 28 Chinese provinces. It is intended to explore the relative role of green technological innovation in driving green financial development in the west and central China, as well as how it influences economic growth in these regions. Ordinary least square (OLS) framework was utilized in mainland China to perform empirical studies by using an econometric estimation. This study claims that China has adopted research-based education system, while those for economic growth and expenditure in the regions while the innovation parts results shows that the  tertiary education were 12.42% and 13.53% versus the 10.50% and 10.6% in the eastern area. The research-based education increases the patents in green innovation and boosts the environmental policy. The financial development led to green technological development and innovation. Green innovation and financial development decrease the emissions, and it is apparent that as environmental regulations stimulate technical development, the superiority of human resources increases. The findings indicate that green financing reduces short-term lending, thus limiting clean energy overinvestment, while the long-term loans have little impact on renewable energy overinvestment, and the intermediary effect is unmaintainable. Meanwhile, the green financial growth will reduce renewable energy overinvestment and increase renewable energy investment productivity to certain amount.

Vertical financial disparity, energy prices and emission reduction: Empirical insights from Pakistan

The economic and environmental aspects of energy production have become important due to the increasing complexity energy sector and envoirnmental pollution, warranting to test the connection between financial imbalances, energy prices and carbon emission. The study aims to test the impact of vertical fiscal imbalances (VFI) on energy prices and carbon emission trends by considering the dual-perspectives of environmental regulation and industrial structure. The empirical outcomes indicated that vertical fiscal imbalances limited the environmental quality of Pakistan. Furthermore, VFI also caused environmental degradation by affecting industrial structure. VFI inhibits the intensity of environmental regulation, promotes the upgrade of industrial structures, both of which cause additional carbon emissions. The study suggest to energy ministries and energy regulation offices to revisit the machinism of energy prices determination and revised machanisim should provide a user-friendly assessment to understand the actual costs associated with the rising concern of environmental pollution. By this, envoirnmental protection maximization and optimal energy conservation is expacted to increase. Based on empirical findings, the study extends the suggestion that vertical fiscal imbalances should be considered an active indicator by the key policy makers and other stakeholders for energy prices determination and environmental quality upgradation.

Does geopolitical risk escalate CO2 emissions? Evidence from the BRICS countries

High levels of CO₂ emissions are extensively cited as one of the main global concerns nowadays. Therefore, researchers have been investigating the factors that affect CO₂ emissions. In the prior literature, several social, economic, and political drivers of CO₂ emissions have been investigated; however, there is a dearth of the literature on the impact of geopolitical risks (GPR) on CO₂ emissions. Hence, the objective of this study is to explore the impact of GPR on CO₂ emissions in the case of the BRICS countries while controlling the effects of population, GDP, non-renewable energy, and renewable energy consumption. The study uses the recently developed GPR index, proposed by Caldara and Iacoviello (2018), and the AMG (augmented mean group) estimator method. The findings document that GPR escalates CO₂ emissions. That is, a 1% increase in GPR escalates CO₂ emissions by 13%. Moreover, it also reports that renewable energy consumption impedes CO₂ emissions. In contrast, GDP, population, and non-renewable energy consumption surge CO₂ emissions. The study also proposes a few policy implications based on the findings: (1) policymakers and government officials should try to limit GPR through peace treaties, agreements, and negotiations; (2) share of renewable energy in total energy consumption should be increased in order to plunge CO₂ emissions.

Assessing the mechanism of energy efficiency and energy poverty alleviation based on environmental regulation policy measures

This work aims to assess multidimensional energy poverty and energy efficiency for environmental policy measures using data envelopment analysis (DEA), a DEA-Like mathematical composite indicator applied on a dataset based on multiple sets of variables from South Asian economies. The multidimensional energy poverty index (MEPI) is computed to analyze the combining effects and energy poverty in these countries. Simultaneously, South Asia's metropolitan areas' population rose by 130 million between 2001 and 2011 and is projected to expand by approximately 250 million by 2030. The findings reveal that endogenous increasing population shocks account for about 72% of energy use. In contrast, the long-term effects of remittance revenue, economic growth, and urbanization on energy use are approximately 20%, 8.25%, and 0.03%, respectively. This work advocates more coordinated and innovative policies to eliminate energy poverty. It can act as a base for policymakers and government officials to make efficient policies and enforce them properly in the regional power sector. Policies should be designed around a smarter use of biomass for cooking, alternate sources for domestic energy production, increased programs for biomass-based cookstoves, and periodic regional-level energy database development.

Does the combining effects of energy and consideration of financial development lead to environmental burden: social perspective of energy finance?

In light of the rapidly growing industrialization in BRICS and G7 regions, thorough energy, financials, and environmental analyses are essential for sustainable financial development in these countries. In this context, this work analyzes the relationship between energy, financial, and environmental sustainability and the regions' social performance. Data from 2000 to 2017 is analyzed through a data envelopment analysis (DEA) like a composite index. Results show China and Brazil's better performance in the region, with a sustainability score of 0.96, India was the third, followed by South Africa and Russia. Japan, the UK, and the USA were the most energy-efficient countries for five consecutive years. A 0.18%, 0.27%, 0.22%, 0.09%, 0.31%, and 0.32% reduction in carbon emission is observed with a 1% increase in R&D costs by Canada, France, Germany, Italy, Japan, and the USA, respectively. This work contributes to the existing literature regarding an eco-friendly sustainable policy design for the G7 countries based on multiple indicators.

Progress in nuclear energy with carbon pricing to achieve environmental sustainability agenda: on the edge of one's seat

The Paris agreement (COP21) emphasized the need to progress toward using low-carbon energy technologies, including nuclear power, that is favorably looked for to meet the challenges to reduce an enormous increase in global temperature to below 2 °C. The cost of carbon pollution is highly induced by the energy sector that damages the global environmental sustainability plan. The alternative and nuclear energy demand is an optimized solution to decrease carbon damages, which can be better work under the imposition of carbon taxes on polluting industries. This study works in a given direction to analyze the role of alternative and nuclear energy, carbon pricing, FDI inflows, fossil fuel combustion, economic growth, and population density on the cost of carbon pollution in a panel of 90 selected countries for a period of 1995-2018. The results confirmed a "nuclear energy-augmented environmental Kuznets curve" with a turning point of 39.974% of total energy demand across countries. The result implies that alternative and nuclear energy initially increases carbon damages. Simultaneously, it decreases at the later stages of atomic energy expansion; thus, nuclear power growth is imperative for long-term sustainable development. A positive relationship is found between carbon pricing and carbon damage, while a negative relationship is between fossil fuel combustion and carbon damage across countries. The results conclude that expansion in nuclear energy would help reduce the cost of carbon pollution to achieve environmental sustainability agenda across countries.

Socioeconomic impacts of domestic biogas plants on rural households to strengthen energy security

The central theme of the study is to assess the socioeconomic impact of household biogas plants on rural households. To this end, the study selected respondents from biogas plant holders and non-holders of biogas plants in rural areas of Muzaffar-Garh. During the field survey, a questionnaire survey was conducted on 40 biogas users in two villages in each Tehsil and 40 non-biogas users in the same village. A survey based on pre-designed questionnaire was conducted, and main data of 320 households in four Tehsil districts (interviewees) in the Muzaffar-Garh region were collected. In this study, biogas is the input variable, while elderly education and total household income are the input socioeconomic variables. Farm productivity, time saving, indoor air pollution, household hygiene, and expenditure are intermediate variables. The output variables include income, health, and education level of minor children aged 2-5 years. Structural equation modeling (SEM) techniques that describe the relationship between input variables and output variables can be used to obtain steadfast results. Based on the estimates, we have observed that BG investments have substantial impacts on farm productivity, time savings, indoor air pollution, household hygiene, and expenditure, which in turn has played a role in improving the status of people. It is concluded that many direct and indirect socioeconomic impacts of holding biogas plants on rural households can be measured. In order to widely promote biogas technology as an alternative energy source nationwide, there must be greater public participation.

Nanostructured Fe2O3/TiO2 composite particles with enhanced NIR reflectance for application to LiDAR detectable cool pigments

Nanostructured Fe₂O₃/TiO₂ composite pigments with improved NIR reflectance were prepared by a homogeneous precipitation method using urea and NH₄OH. The optical and morphological properties of the resulting pigment were investigated by varying the weight ratio of Fe₂O₃ to TiO₂ and the calcination temperature. The resulting composite pigment has a nanostructure in which Fe₂O₃ nanoparticles of 20–30 nm size are well coated on the surface of TiO₂ (∼100 nm) and the reflectance is greatly improved in the wavelength range of 620–1350 nm. The ratio of Fe₂O₃ to TiO₂ and the calcination temperature were optimized to provide both high NIR reflectance and red color, which were 0.1 and 700 °C. As a result, compared with pure Fe₂O₃ (E_g = 2.06 eV, a* = 22.6), the optimized Fe₂O₃/TiO₂ composite pigment (E_g = 2.09 eV, a* = 24.8) showed similar color properties and improved NIR reflectance by about 23.8%. In addition, the Fe₂O₃/TiO₂ composite pigment showed about 62.7% larger reflectance at 905 nm than Fe₂O₃. According to a temperature rise test under IR illumination, the Fe₂O₃/TiO₂ composite pigment was confirmed to have improved heat shielding properties. Therefore, the nanostructured Fe₂O₃/TiO₂ powder could be potentially applied as a LiDAR detectable cool red pigment for autonomous vehicles.

Nanostructured Fe₂O₃/TiO₂ composite pigment particles synthesized by a homogeneous precipitation method showed improved NIR reflectance by about 23.8% and about 62.7% larger reflectance at 905 nm than Fe₂O₃.

A Local Spatial STIRPAT Model for Outdoor NOx Concentrations in the Community of Madrid, Spain

Air pollution control is one of the main challenges facing modern societies. Consequently, the estimation of population, affluence, and technology impacts on air pollution concentrations (STIRPAT modeling) has become the cornerstone of environmental decision-making. Spatial effects are not usually included in STIRPAT modeling of air pollution. However, space matters: accounting for spatial dependencies significantly improves the accuracy of estimates and forecasts, especially (or only) when dealing with small information units rather than with large ones (countries, large regions, provinces in China, counties and states in the USA, etc.). The latter scale is typical in the literature on air pollution due to the difficulties in finding data on its drivers at a true local scale. Accordingly, this paper has a double objective. The first is the estimation of a spatial panel data STIRPAT model, with the spatial units being both very small and also highly autonomous, developed municipalities. The second is to examine whether an environmental Kuznets curve relationship exists between income per capita and NOx concentrations. A case study has been carried out in the Autonomous Community of Madrid, Spain, at the municipal level.

Spatial effects of carbon emission intensity and regional development in China

Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China's provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China's 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China's provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China's carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.

Multi-objective cold chain logistic distribution center location based on carbon emission

The issues of food safety and environmental protection are attracting more and more attention. Everyday, a large number of cold chain products are delivered from suppliers to customers. The cold chain products require refrigeration equipment in delivery and should be delivered to customers as soon as possible. Therefore, the challenge of reducing carbon emission and improving the customer satisfaction should be solved. This study presents the impact of carbon emission, customer satisfaction, construction cost, and operation cost on the location of cold chain logistics distribution center. A multi-objective location model for cold chain logistics distribution center considering carbon emission is established. The carbon emission equivalent cost model considers the dynamic carbon emission during transportation and the static carbon emission of the distribution center. The penalty cost under the time window is introduced into the penalty cost model of customer satisfaction, which represents a multi-objective mixed-integer linear programming problem. A non-dominated sorting genetic algorithm II (NSGA-II) is used to design the program through double-layer composite coding. NSGA-II uses a fast non-dominated sorting approach to reduce the computational complexity of non-dominated sorting. This algorithm uses the elitist control strategy, which does not need to share parameters and is more efficient in the multi-objective optimization process. The numerical results show that the proposed algorithm can generate appropriate Pareto solutions for all objectives.

Non-linear relationship between tourism, economic growth, urbanization, and environmental degradation: evidence from smooth transition models

The study aims to analyze two objectives: first is to explore the non-linear relationship between tourism development, economic growth, urbanization, and environmental degradation, and also to analyze the threshold level of the contribution of tourism development on environmental degradation in top tourist arrival destinations. We applied the newly proposed econometric method panel smooth transition regression (PSTR) framework with two regimes on yearly panel data from 1995 to 2017. Findings suggest that the relationship between tourism development and environmental degradation is non-linear and regime dependent. Furthermore, the findings indicated that the relationship above the threshold level is negative and significant, while below the threshold, tourism development is positive and significant effect on environmental degradation. Tourism development and environmental degradation also exhibit the inverted U-shape relationship meaning that at a particular point, increase in tourism development increases in environmental degradation but after a particular point, increase in tourism development decreases the environmental degradation. The economic growth and urbanization also portray a non-linear and regime-dependent relationship with environmental degradation. The study assists policies and empirical information.

Energy consumption, FDI, and urbanization linkage in coastal Mediterranean countries: re-assessing the pollution haven hypothesis

Foreign direct investment (FDI) and the consumption of non-renewable energy have been on the increase in the coastal Mediterranean countries (CMCs) over the last few decades. Both trigger growth, but the environmental impact could be far-reaching as environmental distortions are mainly human-induced. This study examines the environmental issues facing CMCs. Specifically, we investigate whether the pollution haven hypothesis holds for CMCs. We employ a quantile panel data analysis for CMCs to account for heterogeneity and distributional effects of socioeconomic factors. The result reveals that the influence of FDI on environmental degradation is a function of the indicators utilized and also depends on the initial levels of environmental degradation. The results suggest that the pollution haven hypothesis does not hold for CMCs. However, we also find that energy consumption significantly increases environmental degradation for all indicators and across the observed quantiles. The effects of economic growth and urbanization on the environment were mixed for the different indicators and across quantiles. We recommend that it is pertinent for CMCs to limit their "dirty" energy sources and substitute them with renewables to promote environmental sustainability.

Asymmetrical ARDL correlation between fossil fuel energy, food security, and carbon emission: providing fresh information from Pakistan

The core objective of our study seeks to examine the asymmetrical impact of agriculture, fossil fuel consumption, and food security on carbon emission (CO2) in Pakistan from 1969 to 2018. The current study applied multiple unit root tests (ADF, PP, and KPSS, Z&A) to check data stationarity and structural breaks. We used the population data as a food security proxy indicator. The outcomes disclosed that there is a long-term asymmetric relationship between the variables. The results also verified the atypical response of CO2 to adverse shocks in agricultural value-added. Furthermore, the results showed that population and fossil fuel consumption would further worsen environmental standards. Based on the results of the study, the government needs to take practical steps for active policy-making and assessing ecological challenges in Pakistan.

Income Inequality and CO2 Emissions in Developing Countries: The Moderating Role of Financial Instability

This paper studies the effects of income inequality and financial instability on CO2 emissions in the presence of fossil fuel energy, economic development, industrialization, and trade openness. Moreover, the present study is the first to examine the moderating role of financial instability between income inequality and CO2 emissions. We utilized panel data of forty-seven developing countries for the period 1980–2016 by utilizing the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model. The empirical outcomes in all models indicate that income inequality and industrialization significantly reduce environmental degradation, while fossil fuel, trade openness, and economic growth decrease the quality of the environment. However, financial instability (without interaction term) shows no significant link to environmental quality, whereas (with interaction term) it shows a significant negative effect on CO2 emissions. In addition, the result of the interaction variable reveals that an increase in inequality, ceteris paribus, in combination with the rise in financial instability, is expected to increase pollution. Furthermore, there exists a bidirectional causal association among income inequality, financial instability, fossil fuel, trade openness, industrialization, economic growth, and the interaction variable with CO2 emissions.

Signifying the imperative nexus between climate change and information and communication technology development: a case from Pakistan

The globe has faced technological affluence that enormously revolutionized the lives of humankind. Today, the manufacturing process of the energy sector, production sector, agriculture sector, and service sector is exclusively or partially based on ICT tools. The key intention of this investigation is to find out the impacts of the utilization of ICT on CO₂ emission. However, this investigation also evaluates the influence of investment in ICT and the trade of ICT tools on CO₂ emission. Further, the estimation examined the subsistence of environment Kuznets curve (EKC) theory, for the nation of Pakistan. The investigation employed an autoregressive distributed lag (ARDL) model and found that the utilization of ICT has a negative impact on CO₂ emission. Moreover, the long-run results revealed that the import of ICT equipment is more beneficial for the environment quality of Pakistan. However, ICT apparatus manufactured in Pakistan might produce electronic waste due to non-utilization of green technology. The study reported bidirectional causality between ICT and CO₂ emission. These results point towards that the emergence of ICT in industries and daily life possesses a significant and positive role in climate change in Pakistan. Also, this study corroborates the veracity of EKC in Pakistan.

En route to attaining a clean sustainable ecosystem: a nexus between solar energy technology, economic expansion and carbon emissions in China

This paper probes the nexus between solar energy technology, carbon intensity of energy structures, economic expansion, and carbon emissions (CO₂) throughout 1990-2017 in China. The study utilized the vector auto-regressive (VAR) approach to co-integration testing and vector error-correction models to identify the most effective method for reducing CO₂ emissions. Results from the Granger causality (GC) suggest a unidirectional causality between the variables. The test of impulse response function (IRF) constituted in the VAR technique was also applied in this study. The results indicate that energy structure intensity and economic expansion positively affect carbon emissions, while solar energy technology negatively affects carbon emissions. Simultaneous IRF analysis demonstrated that solar energy technology, energy structure carbon intensity, and economic expansion all have long-term effects on carbon emissions. The study concluded that when the economy expands, it influences CO₂ emissions. Also, there exists a positive impact on CO₂ emissions from the number of solar patents, but was seen to be decreasing gradually. The policy implications were also stated.

Spatiotemporal characteristics and driving forces of construction land expansion in Yangtze River economic belt, China

With rapid economic and population growth, construction land expansion in Yangtze River economic belt in China becomes substantial, carrying significant social and economic implications. This research uses Expansion Speed Index and Expansion Intensity Index to examine spatiotemporal characteristics of construction land expansion in the Yangtze River economic belt from 2000 to 2017. Based on a STIRPAT model, driving forces of construction land expansion are measured by Principal Component Analysis and Ordinary Least Square regression. The results show that: (1) there is a clear expansion pattern regarding the time sequence in provinces/cities of the Yangtze River economic belt, with rapid expansion in the initial stage, moderate expansion in the middle stage and rapid expansion in the later stage. (2) Spatial analysis demonstrates first expansion in the lower reaches in the early stage, rapid expansion of the upper reaches in the middle and later stage, and steady expansion of the middle reaches throughout the research period. (3)There are statistical significant correlations between construction land expansion and GDP, social fixed asset investments, population at the end of the year, population urbanization rate, per capita road area, and number of scientific and technological professionals as well as secondary and tertiary industry values. Of these factors, GDP, social fixed asset investments, population urbanization rate and second industry value are important common driving forces of construction land expansion in this region. The research findings have significant policy implications particularly on coordinated development of urban agglomerations and sustainable industry upgrading when construction land expansion is concerned.