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This study marks the pioneering use of time-series econometric techniques and methods, including ARDL, FMOLS, and CCR framework, to examine the short-run dynamic and long-run impacts of climatic change and agricultural technologies on wheat production in India. The research covers the period from 1991 to 2018, thereby spanning nearly three decades. To enhance the assessment of factors influencing wheat production, we incorporated agricultural employment, agricultural credit, and cultivated land into the empirical model. By doing so, we aimed to gain a deeper understanding of the influence of fundamental factors on wheat production dynamics during the study period. In addition, the Granger causality test was employed to determine causational direction among the underlying variables. Results indicate that climatological factors such as precipitation pattern and carbon emissions contribute to an increase in wheat production. In addition, the results indicate that advanced technology has considerably contributed to the increase in wheat production. The ARDL method's long-term results were confirmed and validated by econometric techniques including FMOLS and CCR. According to Granger's estimation of causality, the relationship between farm equipment, agricultural labor, and wheat crop yield is bidirectional. The findings regarding causality indicate that climatological factors had a significant impact on wheat production. To mitigate this issue, the government should develop new crop varieties that are well-suited to the agroclimatic conditions. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions have an impact on agricultural activity in the West African region, which is largely agrarian. As a result, most of the countries in the region are food insecure, and many people are malnourished. There is a lack of a multidisciplinary perspective that analyzes greenhouse gas emissions and proposes mitigation strategies to help the region contribute to reducing greenhouse gas emissions. To mitigate this challenge, this study examined the threshold beyond which agriculture will reduce greenhouse gas (measured by carbon dioxide emissions, methane emissions, and nitrous oxide emissions) in West Africa. The study used a sample of 16 West African nations from the years 2002 to 2018 and the panel threshold estimator for the data analysis. The results showed that agriculture will reduce carbon and nitrous oxide emissions beyond threshold values of 3.287 representing about $26.762 million, and 3.683 representing about $39.766 million, respectively. However, the threshold impact of agriculture on methane emissions in West Africa was insignificant. The results also demonstrated that while there is no causative association between agriculture and methane emissions, there is a unidirectional causation link between carbon emissions and agriculture and nitrous oxide emissions and agriculture, respectively. The study suggested agricultural investments in both traditional high-yielding technology and adaptation are needed to meet the development goal of climate change. [ABSTRACT FROM AUTHOR]

The primary objective of the current study is to examine the social issue of energy poverty from a multi-dimensional perspective in Pakistan’s national, sub-national, and urban-rural regions. The universally attributed Alkire and Foster indexing approach and globally organized PSLM (Pakistan Social and Living Standard Measurement) data from 2010–2011 to 2019–2020 were used to calculate numerical results. The study’s empirical findings emphasize that multiple-attribute energy poverty is primarily a rural phenomenon in Pakistan and its provinces. Furthermore, the time-variant situation reveals that with a shorter (longer) period, energy poverty exhibits a hybrid (declining) trend for Pakistan as a whole, its four provinces, and its regions. Finally, decomposability analyses of population subgroups and key dimensions show that households in two provinces (Sindh and KPK) and three attributes (cooking, home appliances, and entertainment) are the primary contributors to multi-dimensional energy poverty. Hence, to achieve the UN’s Millennium Development Goals, i.e., SDG7, by 2030, several measures must be implemented rapidly, particularly in rural areas and the provinces, especially Sindh and KPK, such as providing easy access to modern energy services, reducing load shedding and energy shortfalls through advanced technology adoption, and improving households’ living standards by reducing financial poverty. All of these solutions will assist in rapidly reducing energy poverty at all levels, paving the way for achieving the zero energy poverty goal (SDG7) by 2030. [ABSTRACT FROM AUTHOR]

This article explores the impact of farmers' livelihood assets and adaptation approaches on livelihoods. We proposed a new paradigm for the sustainable livelihoods of smallholders based on the grounded theory, to exploit seven household assets and used mixed methodological approaches of growers' livelihood assets and climatic adaptation strategies based on farmers' livelihood concerns in Sindh province of Pakistan and to better understand smallholder farmers' sustainable livelihoods and applied a partial least square path modeling. Considering central critical paths, psychological assets (PsyA) entail mediational factors into the sustainable livelihoods index. The findings show that the essential support of the livelihood of farmers' social, cultural, economic, and human assets positively impacts sustainable livelihoods, though physical and natural assets depict a nonsignificant impact on SLI. However, human and financial assets show comparatively substantial effects on smallholders' adaptation strategies; consequently, physical, social, and natural assets reveal significant impacts on farmers' livelihoods. The study further indicates that PsyA can, directly and indirectly, boost the human and physical assets and exploit the farmers' sustainable livelihoods index successfully. Lastly, this research proposed policy suggestions for poverty reduction and sustainable livelihoods of small-scale growers. [ABSTRACT FROM AUTHOR]

East Africa has a substantially greater rate of food insecurity than other regions of the world. Scenarios of climate change and other macroeconomic variables are important contributors to food insecurity in East Africa. Using data spanning from 1990 to 2020, this study looked into the influence of carbon dioxide (CO2) emissions, economic growth, population growth, trade openness, and agricultural employment on food security in the East Africa. The fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) models were used in this study. The heterogeneous panel cointegration test's findings indicated that the study variables have an equilibrium long-term connections. The estimation findings from the FMOLS and DOLS models showed that an increase in CO2 emissions increases food security in the East Africa over the long term. According to other findings, long-term food security is positively impacted by economic expansion, population growth, trade openness, and employment in agriculture. However, trade openness has a detrimental long-lasting effect on food security. Future research directions, research limitations, and policy implications are discussed. [ABSTRACT FROM AUTHOR]

Promoting the green development of resource-based cities is an essential way to achieve sustainable regional economic development. Based on 2009–2019 panel data of the Yellow River Basin cities, this study adopts the super-directional distance function model to measure the green development efficiency of these selected cities. Furthermore, based on the Malmquist-Luenberger index, this paper focuses on the dynamic change trend of green development efficiency and internal driving factors. Furthermore, the Tobit model is used to specifically explore the influencing factors affecting the green development of cities. The findings suggested that the green development efficiency of selected cities falls in the middle to high range and that the efficiency varies among all cities in the Yellow River Basin. Likewise, technical efficiency improvements and technological progress drive development efficiency, and the former contributes more to green development. However, financial development, energy structure adjustments, and environmental regulation can strongly contribute to the green development of cities, and each influencing factor has obvious temporal and regional differences. This paper proposes appropriate policy suggestions to promote the coordinated development of the economic development and environmental protection of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

China seems ambitious to achieve a "carbon emissions peak" before 2030 and "carbon neutrality" before 2060. To deal with this emissions mitigation plan, technology innovations are regarded as a crucial factor. However, considering its rebound effect (CO2 emissions driving effect) through economic growth, technology innovations might not prove a promising contributor to CO2 reduction. Therefore, there is a need to investigate further the nexus between technology innovations and CO2 emissions for conclusive debate. Based on the data of 215 cities in China, this paper uses mediating effects model to investigate the direct and indirect impacts (through economic growth and industrial structure transformation) of technology innovations on CO2 emissions from a microeconomic perspective. The main results suggest that technology innovations generally increase CO2 emissions in China both directly and indirectly. The impact of technology innovations and economic growth on CO2 emissions indicated the EKC characteristics. Furthermore, the contributions of technology innovations to CO2 emissions are distinguished in different regions. Thus, there is an urgent need for China to promote innovations in "clean technology" and to transform industrial structure to the tertiary industry to achieve the targets of carbon neutrality and emissions peaking. [ABSTRACT FROM AUTHOR]

Clean fuels and technology-based energy is an essential source to achieve sustainable economic growth and development. Therefore, the relationship between all types of poverty and other socioeconomic indicators has been studied extensively; nevertheless, clean fuels and technology-based energy poverty, adjusted for carbon emissions, has not been studied. The current study examines the impact of clean fuels and technology-based energy poverty on carbon emissions (Co2e). Using System-Generalized Method of Movement (SGMM) estimators, this study utilized panel data from eighteen developing countries in Asia from 2006 to 2017. The empirical findings obtained from econometric model suggest the presence of clean fuels and technology-based energy poverty and its curse on environment, i.e., energy poverty positively affects Co2e growth in Asian developing countries. Furthermore, economic growth (GDP), trade, and population are also positively associated with Co2e growth and negatively affect environmental quality. Based on the empirical findings of the current study, we recommend robust policy implications that the governments of targeted countries should invest more to increase clean fuels and technologies. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Among all other leading challenges of this century, climate change affects people's livelihoods, particularly those residing in rural areas. This study is the first attempt to assess the Long-run (LR) and Short-run (SR) impacts of climatic factors, namely CO2 emissions, temperature, and precipitation on maize production in Nepal from 1983 to 2016, with the incorporation of cultivated area, fertilizers use, and credit supply as important input factors of maize production. To analyze the time-series dataset, we applied the Autoregressive distributed lag (ARDL), Vector error correction model (VECM), Impulse response function (IRF), and Variance decomposition (VD) methodologies. The results reveal that CO2 emissions and temperature decreased maize production in the short- and long-run while precipitation improved maize production in both cases. Also, cultivated area and fertilizers use significantly contributed to maize production in both periods. The results of the VECM causality indicate that rainfall and cultivated area have two-way causal associations with maize production. The CO2 emissions and credit represent unidirectional causality with maize production in the short-run. Besides, all variables share a significant long-run connection. Finally, the IRF and VD outcomes suggested that the effects of climatic and non-climatic factors are consistent on maize production. The policies recommend that better environmental quality is crucial for sustainable maize production in Nepal. [ABSTRACT FROM AUTHOR]

This paper examines the impact of climate variables and financial development on agricultural value-added and cereal production in selected Southeast Asian economies from 1970 to 2016. The current research applies second-generation advanced techniques to accomplish robust and reliable outcomes. The findings from the FM-OLS estimation disclose that climatic factors, for instance, CO2e and average temperature, impact both agricultural value-added and cereal production negatively, and financial development has an inverted U-shaped influence on both agricultural value-added and cereal production. Meanwhile, other important factors, including cropped area, income level, and rural labor force, significantly improve agricultural value-added and cereal production. Furthermore, the FM-OLS estimator's D-H panel causality test outcomes are reliable. The findings of our study reveal that both the short- and long-run risks of climatic changes to the agricultural sector pose a large-scale threat to food security in Southeast Asian economies. As a result, a robust and stable financial development in terms of governance of climate change finance in the agriculture sector must be achieved to enhance farmers' ability to adapt to current and future climate change adverse impacts. [ABSTRACT FROM AUTHOR]

In this research two cases of infrastructure development (Chotiari and Diamer Bhasha Dams) from Pakistan were studied in terms of a superposition of land uses and their consequences. For this purpose, we obtained qualitative information from both primary as well as secondary sources. Primary data were collected through a partially developed questionnaire from pre-selected experts of various professional backgrounds. National and regional dailies along with other published literature were used as a secondary source of information. The findings have identified the key groups of stakeholders and their relative social power at different levels of governance. The results further highlight that unfair land acquisition, improper displacement, mismanagement in compensation, etc., have caused negative impacts on local people and the surrounded environment. The article further emphasizes governance issues and conflicts among different actors due to the project. Finally, we recommend several actions to prevent strong opposition and conflicts in the infrastructural project in developing countries, like the enhancement of the capacities and the capabilities of the local population, the diffusion of information and the involvement of stakeholders, and the application of technical tools and devices. [ABSTRACT FROM AUTHOR]

In 2009, Pakistan introduced a subsidized biogas program to disseminate clean and affordable energy in rural areas. However, the adoption rate did not appear as expected. Therefore, the present study aims to examine the determinants behind the non-adoption of biogas digesters. Detailed information via a structured questionnaire was obtained from biogas adopters and non-adopters, and analyses were performed in two levels. Initially, the study used the multinomial logit regression to elicit the non-adoption behavior. Results depicted family size, risk aversion, cook education (women), kitchen (inside the home), home structure, and non-availability of inputs as significant factors behind non-adoption. Detailed analysis also revealed that the factors like the age of household head, area of the house, cook education (women), location of the kitchen (inside the home), and the distance of the house to the bus stop (minutes) lead towards the potential adoption of biogas. The study also investigated the potential factors behind the long-term sustainability of biogas digesters with the help of binary logit. The results depicted that the kitchen's location (inside the house), cook education (women), training, and subsidy significantly and positively impact the biogas plant's probability of being functional. Based on these results, the study suggested that policymakers increase women's education in rural areas, provide a subsidy to reduce costs and risk, and arrange training for adopters. [ABSTRACT FROM AUTHOR]

The discussion concerning whether and how economic complexity (ECI) affects ecological footprint (EFP) has gained researchers' consideration, while there are slight empirical evidence to support the subject matter. In the support of theoretical argument, this study provides empirical evidence by investigating the impact of ECI on EFP along with the role of disaggregated energy consumptions by using a panel dataset of G-7 economies between 1996 and 2019. To this end, we applied panel techniques of Fully-Modify OLS and Dynamic-OLS models for cointegration analysis. The results obtained from Fully-Modify OLS and Dynamic-OLS models reveal that ECI deteriorates environmental quality by increasing EFP, while renewable energy reduces ecological pollution by decreasing EFP. In addition, the increasing demand for non-renewable energy and economic growth both degrades environmental quality in G-7 countries. More interestingly, the non-linear (ECI2) relationship between ECI and EFP confirms a U-shaped association (EKC hypothesis), which suggests that after achieving a certain threshold level, economic complexity mitigates environmental degradation in G-7 economies. The empirical results also suggest that other control variables such as population growth, inflation rate, foreign direct investment, and total trade intensity lead to environmental degradation by increasing ecological footprint. Based on empirical results, the following important policy implications are drawn; first, G-7 economies should speed up the level of economic complexity along with renewable energy consumption to protect environmental quality and maintain sustainable growth and development. Secondly, the governments of G-7 countries should introduce greener technologies and promote production that are environmental friendly for drastic reduction in environmental unsustainability. [ABSTRACT FROM AUTHOR]

This study is the first attempt to examine the effects of changing climate and financial development on rice cultivation in the context of agrarian economy like Thailand from 1969 to 2016. The current study also uses other important variables, such as cultivated area, organic fertilizers usage, and rural labor to determine the long-term connection amid variables. In this study, we applied several econometric techniques, for instance the autoregressive distributive lag–error correction model (ARDL–ECM), vector error correction model (VECM), impulse response functions (IMFs), and variance decomposition (VARD) method to estimate the most reliable and robust outcomes. The empirical results showed that in the long- and short-run, there is a reduction in rice cultivation as temperature increase. The carbon dioxide (CO2) positively affects rice cultivation in the long-run, while this association is negative in the short-run. The findings further revealed that in the long- and short-run, domestic credit provided by the financial sector significantly positive improved rice cultivation, while domestic credit to private sector by banks negatively affects rice cultivation. The important input factors, including cultivated area, organic fertilizers usage, and rural labor significantly positive contributed toward rice cultivation in the long- and short-run. The calculated long-run causal connection of all the studied variables with rice cultivation is validated. The estimated short-run causal relationship is unidirectional among temperature, CO2 emissions, financial development, rural labor, and rice cultivation. In addition, our outcomes are robust and also verified by IMFs and VARD method. The study offers some important policy suggestions to increase rice production with the help of sound and well-developed financial systems and climate controls. [ABSTRACT FROM AUTHOR]

In recent years, environmental change has arisen as a ubiquitous problem and gained environmentalist's attention across the globe due to its long-term harmful effects on agricultural production, food supply, water supply, and livelihoods of rural households. The present study aims to explore the asymmetrical dynamic relationship between climate change and rice production with other explanatory variables. Based on the time series data of India, covering the period 1991–2018, the current study applied the nonlinear autoregressive distributed lag (NARDL) model and Granger causality approach. The results of the NARDL reveal that mean temperature negatively affects rice production in the long run while positively affecting it in the short run. Furthermore, positive shocks in rainfall and carbon emission have negative and significant impacts on rice production in the long and short run. In comparison, negative rainfall shocks significantly affect rice production in the long and short run. Wald test confirms the asymmetrical relationship between climate change and rice production. The Granger causality test shows feedback effect among mean temperature, decreasing rainfall, increasing carbon emission, and rice production. While no causal relationship between increasing temperature and decreasing carbon emission. Based on the empirical investigations, some critical policy implications emerged. Toward sustainable rice production in India, there is a need to improve irrigation infrastructure through increasing public investment and to develop climate-resilient seeds varieties to cope with climate change. Along with, at the district level government should provide proper training to farmers regarding the usage of pesticides, the proper amount of fertilizers, and irrigation systems. [ABSTRACT FROM AUTHOR]

The present study aims to investigate the effect of climatic and non-climatic factors on rice production by employing an annual time series data from the period of 1970 to 2018. The study employed an ARDL (Autoregressive Distributed Lag) approach, and the long-term equilibrium linkages between the variables have been discovered. Additionally, the study also used a regression model to determine the robustness for the authentication of results. The Fully Modified Ordinary Least Squares (FMOLS), Canonical Cointegration Regression (CCR) methods, and the VECM (Vector Error Correction Model) technique confirmed the long-run causal relationships amid the variables. The empirical results further revealed that climatic factors including annual temperature negatively affect the rice crop production, while carbon dioxide emission positively influenced via long-run. Similarly, non-climatic factors like area under rice crop, fertilizer consumption, labor force, and water availability affect the rice production positively in the long-run analysis. Finally, the pairwise Granger causality test revealed that both climatic and non-climatic variables had a substantial impact on rice yield in Pakistan. Based on the study's findings, the government and policy makers should formulate alleviation polices to tackle with harsh effects of climate change and consistent adoption of measures to secure overall agricultural production including rice crop because it is a country stable food. [ABSTRACT FROM AUTHOR]

This research attempts to evaluate the linkage among climatic change factors such as average temperature and rainfall patterns and non-climatic factors such as the area under major yield crops, fertilizer consumption, and formal credit on major food crop yield from 1985 to 2016 in Pakistan. For the first step, we checked the stationarity of the series by utilizing the unit root tests. An autoregressive distributed lag (ARDL) model was employed to identify the linkages between variables after verifying the properties over a specific period of time. The consequences of this study confirmed the long-run association between climatic and non-climatic factors to the major food crop yield in Pakistan. Furthermore, the outcomes of the study revealed that temperature has a diverse impact on major food crop yields. Whereas, the area under major food crops, average rainfall, fertilizer consumption, and formal credit have a positive impact on major food crop yield in Pakistan. For the second step, we used the Granger causality test to verify the causal linkage for the variables. The outcomes reveal a significant effect of climatic and non-climatic factors on major food crop yield. The bidirectional causality causal associations are found to be significant among variables including average temperature, fertilizer consumption, and formal credit disbursement. The empirical results further indicated that major food crop yields are more affected by climatic factors such as average temperature as compared to non-climatic factors. Based on the study findings, few recommendations are made to cope with factors of climate change. Invent such agricultural-specific adaptation policies for farmers which possess the ability and resilience to tackle climate change. Research and development in agriculture should focus on major varieties of food crops that can endure high temperatures. The agriculture industry will be able to sustain long-term production and distribution efficiency attributable to these strategies. [ABSTRACT FROM AUTHOR]

The underpinned study examines the effects of climatic and non-climatic factors on Indian agriculture, cereal production, and yield using the country-level time series data of 1965–2015. With the autoregressive distributed lag (ARDL) bounds testing approach, the long-term equilibrium association among the variables has been explored. The results reveal that climatic factors like CO2 emissions and temperature adversely affect agricultural output, while rainfall positively affects it. Likewise, non-climatic factors, including energy used, financial development, and labor force, affect agricultural production positively in the long run. The estimated long-run results further demonstrate that CO2 emissions and rainfall positively affect both cereal production and yield, while temperature adversely affects them. The results exhibit that the cereal cropped area, energy used, financial development, and labor force significantly and positively impact the long-run cereal production and yield. Finally, pairwise Granger causality test confirmed that both climatic and non-climatic factors are significantly influencing agriculture and cereal production in India. Based on these results, policymakers and governmental institutions should formulate coherent adaptation measures and mitigation policies to tackle the adverse climate change effects on agriculture and its production of cereals. [ABSTRACT FROM AUTHOR]

This paper examines the effect of climate change and financial development on agricultural production in ASEAN-4, namely Indonesia, Malaysia, the Philippines, and Thailand from 1990 to 2016. Further, we explore the role of renewable energy, institutional quality, and human capital on agricultural production. Since the shocks in one country affect another country, we use second-generation modeling techniques to find out the relationship among the variables. The Westerlund (2007) cointegration tests confirm long-run relationship among the variables. The results from cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model reveal that climate change negatively affects agricultural production; on the other hand, renewable energy, human capital, and institutional quality affect positively agricultural production. Moreover, renewable energy utilization, human capital, and intuitional quality moderates the effect of carbon emission on agricultural production. In addition, a U-shaped relationship exists between financial development and agricultural production, suggesting that financial development improves agricultural production only after reaching a certain threshold. Hence, this study suggests that ASEAN-4 countries must adopt flexible financial and agricultural policies so that farmers would be benefitted and agricultural production can be increased. [ABSTRACT FROM AUTHOR]

Based on the comparative perspective of environmental regulation performance, this paper uses the threshold effect model to analyze the nonlinear characteristics of the impact of environmental regulation on economic development and environmental pollution and explore the dual performance of environmental regulation on economic and environmental development. The results are fourfold: Firstly, the intensity of fiscal expenditure on energy conservation and environmental protection has a restraining effect on environmental pollution, but it has a crowding-out effect on economic growth. Therefore, it is impossible to achieve a win-win situation between ecological protection and economic development by increasing the intensity of financial expenditure on energy conservation and environmental protection. Secondly, giving more financial rights to local governments in eastern China may help to reverse the non-win-win situation of the economy and the environment. Thirdly, increasing the environmental protection awareness of local governments will help to achieve a win-win situation. It is more effective to achieve a win-win situation of ecology and economy using constraint regulation than incentive regulation. Finally, the economic development level of the last period has different threshold effects on the current environmental pollution in the eastern and western China. Thus, the impact of environmental regulation lags. We conclude that we should give full play to the role of the environmental regulation, stimulate the endogenous power of environmental system, accurately implement the key differentiation strategy, and promote the win-win situation of ecological protection and economic development. [ABSTRACT FROM AUTHOR]

This study investigates the impact of the mechanism of green public consumption policy on environmental equity and provides a feasible reference for the rational formulation of corresponding policies for China. Establish a mediation effect model using the stepwise regression and bootstrap analysis method, this study explores the direct and indirect effects of fiscal expenditure intensity on environmental equity. The results revealed that increasing fiscal expenditure on energy conservation and environmental protection cannot directly and significantly affect environmental equity, but it has a significant indirect positive impact on environmental equality through fiscal decentralization. It is also found that the impact of green public consumption policy on environmental equity varies in the eastern, central and western parts of China. Our findings indicated that the expenditure of energy conservation and environmental protection indirectly affects environmental equity through fiscal decentralization. The willingness of the government to protect the environment regulates the impact of green public consumption policies on environmental equity. The phenomenon of feeding the east from the west of resources and environment is obvious. According to the characteristics of regional development, we should formulate energy conservation and environmental protection policies and adjust the direction of policies to promote regional environmental equity. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on yield of selected cereal crops (wheat and maize) in the northern climatic region of Khyber Pakhtunkhwa (KP) province of Pakistan for the period 1986–2015. The first-generation unit root tests such as the Levin, Lin, and Chu (LLC), augmented Dickey-Fuller (ADF)–Fisher, and the second-generation unit root tests such as cross-sectional augmented Im-Pesaran-Shin (CIPS) and cross-sectional ADF (CADF) are used to check stationarity of the series. The cointegration among the variables is discovered via Pedroni test and Westerlund method. The long- and short-run impacts of climatic variables (average precipitation, maximum temperature, and minimum temperature) on yield of wheat and maize crops are assessed through the autoregressive distributed lag (ARDL) model. The empirical findings reveal that average precipitation has a significantly positive impact on yield of both crops in long- as well as short-run. The results further reveal that the effect of average minimum temperature on both crops is insignificant in long-run. However, the short-run effect of average minimum temperature is significantly positive on yield of maize crop but insignificant on yield of wheat crop. In long-run, an increase in average maximum temperature negatively affects crop yield. In short-run, however, it positively affects the yield of wheat and maize crops. The study recommends that increase in area under cultivation, development of advanced irrigation system, and farmers' access to metrological information will help in lowering the drastic impacts of climate change on crop productivity. [ABSTRACT FROM AUTHOR]

Since the development of the service sector and renewable energy reduce fossil-based energy consumption which mitigates CO2 emissions and this nexus provides a better understanding of the environmental sustainability. Considering the substantially increasing contribution of service sector and tremendous potential for renewable energy in ASEAN5 countries, leaning forward from ASEAN's energy and growth nexus, this study examines the impact of service sector contribution and renewable energy on the environmental quality of ASEAN5 using annual data from 1990 to 2018. The results of the fully modified ordinary least squared, dynamic ordinary least squared, and canonical co-integrating regressions depicted that the service sectors of Thailand, the Philippines, and Singapore augment CO2 emissions; however, the service sectors of Malaysia and Indonesia could reduce CO2 emissions. The increasing share of renewable energy can enhance environmental quality, but its magnitude varies in ASEAN5 economies; non-renewable energy, population, and economic development deteriorate the environment. Our results confirm the existence of environmental Kuznets curve in all the ASEAN5; the Gregory-Hansen test confirmed that results are robust. Finally, the Granger causality designated that economic development and non-renewable energy have a significant causal relationship with CO2 emission of ASEAN5 countries. These findings suggest that the ASEAN5 economies need to optimize their economic structure for promoting sustainable development in the long run. Graphical abstract [ABSTRACT FROM AUTHOR]

This work analyzed the long-run (LR) and short-run (SR) effects of renewable and non-renewable energy (RE and NRE) usage, economic development (ED), agricultural value-added (AVA), and forestry area (FA) on the environmental quality (EQ) in China spanning from 1990 to 2015. The autoregressive distributed lags (ARDL) bounds testing method and the Johansen cointegration approach are applied to produce empirical estimates. The empirical results of the ARDL and the fully modified ordinary least square (FMOLS) estimators established that renewable energy usage and forest area reduce CO2 emissions and improve the environmental quality, while non-renewable energy consumption, economic development, and agricultural output increase the level of CO2 emissions in China. The robustness of outcomes is checked through the Granger causality test, impulse response function (IRF), and variance decomposition method (VDM) suggesting that fossil fuel usage in the agriculture production process is mainly accountable for China's CO2 emissions. These findings have inherent policy implications for the central and local Chinese government, which are exhibited in the "Conclusions" section. [ABSTRACT FROM AUTHOR]

This study assessed the long-run (LR) and short-run (SR) impacts of climatic and non-climatic factors, i.e., CO2 emissions (CO2e), average level of temperature (ALT), average level of precipitation (ALP), area harvested of wheat and rice crops (AHW and (AHR), domestic credit (DCR), and agricultural labor (ALB) on wheat and rice production (WP and RP) in Turkey by using annual time series data ranging from 1980 to 2016 and by employing several econometric techniques. The autoregressive distributed lag-bounds (ARDL) approach and the Johansen and Juselius cointegration (JJC) test confirmed a valid long-term connection among underlying variables. The estimation results from the ARDL model reveal that climatic factors such as CO2 emissions and temperature adversely affected wheat production in the long run as well as in the short run, whereas precipitation positively improved wheat production in both periods. Further results indicate that non-climatic factors like area harvested of wheat and domestic credit positively and significantly enhanced wheat production in the long run and short run. Similarly, CO2 emissions also adversely affected rice production in both periods, while temperature and precipitation positively contributed towards rice production in both cases. In addition, area harvested of rice positively and significantly boosted rice production in the long run as well as in the short run, while domestic credit negatively influenced rice production in the long run but in the short run positively improved rice production. Additionally, the outcomes of the VECM Granger Causality for both rice and wheat production confirm that both climatic and non-climatic variables have a strong influence on the production of both crops. This study found that climate change has a deleterious influence on both wheat and rice production; therefore, the study suggests that temperature-resistant varieties of both crops should be developed and introduced by agricultural research institutions. In addition to this, up-to-date information is more needed related to climate change, and in the farming communities, it should be provided by agricultural extension workers. [ABSTRACT FROM AUTHOR]

As an important production factor, land resources significantly impact green technology innovation. However, the misallocation of land resources caused by the government's "second-hand" land supply strategy has become increasingly prominent, which will adversely affect green technology innovation by affecting the allocation of innovative elements. Based on the research data of 252 cities in China from 2008 to 2017, this paper uses panel space measurement estimation and panel threshold estimation empirical methods to test the theoretical hypothesis of the impact of misallocation of land resources on green technology innovation. The study finds that local or neighboring land resources' misallocation has a hindering effect on local green technology innovation. Furthermore, the misallocation of land resources has a threshold effect on the impact of green technology innovation. The relatively high level of local economic development and environmental regulation reduces the restraining effect of the misallocation of land resources on green technology innovation, and vice versa. Therefore, local governments should optimize the allocation of innovative elements, accelerate the construction of an efficient and market-oriented green technology innovation system, reduce the excessive intervention in land resources, and enhance the vitality of innovation entities to improve the level of green technology innovation. [ABSTRACT FROM AUTHOR]

This research investigates the dynamic interactive associations among sustainable investment in the energy sector, air pollution, and sustainable development. To this end, it employs a "one-step" system-generalized method of moments (GMM) and "one-step" differential-GMM estimators, covering the period between 1996 and 2017. In this context, it utilizes the simultaneous equations of the dynamic panel data model for panel data of 27 Chinese provinces and municipalities. We have developed a new model of sustainable development, which incorporates sustainable investment in the energy sector and air pollution to offer a robust theoretical foundation for considering the underlying relations. The system-GMM estimator is used for the full data set; however, differential-GMM is utilized for the subsets of data, in order to tackle the small sample bias problem. The empirical outcomes provide several vital insights in that they yield mixed findings for the aggregated sample and subsets of data. For example, a two-way causal relationship occurs for all the panels, except the central part (medium development regions), between sustainable investment in the energy sector and sustainable development. Contrary to this, causality runs from air pollution to sustainable investment in the energy sector in a full data set and the central part (medium dev.). Nevertheless, the opposite is true in the case of the eastern part (most developed regions) of China. Still, the same relationship runs in either direction in the case of the western part (least developed regions). On the other way around, the feedback hypothesis of causality is confirmed, across all the samples, between air pollution and sustainable development. Hence, sustainable development and air pollution are overwhelmingly interdependent, in the country as well as the province and municipality level of the Chinese economy. [ABSTRACT FROM AUTHOR]

This study investigates the heterogeneous causal linkages between urbanization, the intensity of electric power consumption, water-based pollutant emissions, and GRP in regional China by developing an urbanization-augmented "Stochastic Impacts by Regression on Population, Affluence, and Technology" (STIRPAT) model. A whole country panel of 29 provinces as well as region sub-panels of China, for the period 1999 to 2018, are estimated employing common correlated effects mean group approach (CCEMGA), which offers robustness against heterogeneous characteristics and cross-sectionally dependent series. From the theoretic modeling aspect, the intensity of electric power consumption and urbanization have been introduced as the determinants of water-based pollutant emissions in the STIRPAT modeling framework. Based on empirical results, first, GRP growth has shown appealing behavior in the form of its heterogeneous impacts on water-based pollutant emissions growth in the case of different regions. For instance, its impact is noted to be positive and statistically significant for the western region, which turned positive but statistically insignificant for the intermediate region. And it further turned significantly negative in the case of the eastern region. We call this phenomenon as "development level-based emission mitigation effect." Second, in terms of the impact of GRP growth on urbanization, the "development-based urbanization ladder effect" has been found. Based on heterogeneous causal links, firstly, the existence of a positive bilateral causal link between the intensity of electric power consumption and GRP growth and urbanization and GRP growth has been validated. Secondly, a positive unidirectional causal link emerged from urbanization to the intensity of electric power consumption and water-based pollutant emissions growth. Thirdly, the causal connection between GRP growth and water-based pollutant emissions growth remained very interesting and of mixed nature. Based on empirical findings, useful policies are extended. [ABSTRACT FROM AUTHOR]

This study aims to explore the dynamic association among crop production, livestock production, power consumption in agriculture, forest area, and carbon dioxide (CO2) emissions. Based on the annual data of China, spanning the period 1990 to 2016, the study applied the auto-regressive distributed lag (ARDL) bounds testing approach. In addition, the fully modified ordinary least squares (FMOLS) canonical cointegration regression (CCR) and the Granger causality tests are employed to check the robustness of the ARDL estimations. The ARDL-bounds testing approach indicated that all variables share a long-run connection. The long- and short-run ARDL estimations confirmed that crop production, as well as livestock production, has a significant positive effect on CO2 emissions in both cases. However, power consumption in agriculture and forest area has a negative effect on it, indicating that both variables reduce CO2 emissions in the long and short run. These results stood robust under various regression estimators and confirmed the findings of the ARDL method. Additionally, the results of the causality approach specified that a unidirectional causality is running from crop production, power consumption in agriculture, and forest area to CO2 emissions. The causality between livestock production and CO2 emissions is bidirectional. Therefore, the directions of this connection also validate the outcomes under various techniques used for robustness. These findings suggest that the government must reconsider its policies related to agricultural and livestock production and adopt environment-friendly practices in the agriculture sector that may reduce the carbon footprints in the long run. [ABSTRACT FROM AUTHOR]

This study examines the interaction between energy poverty, employment, education, per capita income, inflation, and economic development using panel data for seven South Asian countries. The present study uses panel data spanning the period from 1995 to 2017, panel cointegration, autoregressive distributed lag (ARDL), and penalized quantile regression (PQR) estimators to test for cointegration in the long-run. The estimated results reveal that both panel cointegration approaches (Pedroni and Johansen–Fisher) demonstrate the existence of the long-term relationship between energy poverty, employment, education, per capita income, inflation, and economic development. The ARDL estimates show that energy poverty has a negative influence on economic development in both the long-run and the short-run. The results provide support for economic, social, and environmental policymakers in their decision-making. This study suggests that, in relation to financing the green and low-carbon economy concept, both the public sector and private industries need to make further efforts to use modern, energy-efficient, and green technologies, which are beneficial both for economic progress as well as managing the ecological degradation process. [ABSTRACT FROM AUTHOR]

In this study, we attempt to investigate the relationship between electricity consumption, foreign direct investment (FDI) and economic progress in Pakistan during the period from 1997 to 2017. We applied the unit root tests to cointegration approach with the vector error correction model (VECM) approach. We also authenticated the robustness of the outcomes with the help of the regression methods. The main findings indicated that variables are cointegrated in the long-run and the VECM approach verified long-run causal links among the variables. The short-run causality is running from electricity consumption and economic development to FDI. The results remained uniform under the fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and the autoregressive distributed lag (ARDL) estimations. Correspondingly, these three methods ratified that there is a significant long-run linkage between electricity consumption and the economic development of Pakistan. Additionally, the diagnostic assessments fixed that results are free from correlations and models are stable. Thus, based on these results, electricity generation and consumption, as well as FDI, is crucial for the economic progress of Pakistan. The priority should be given to promote the optimum use of available resources to generate energy, and FDI in the energy sector should be attracted through various incentives to support the economic advancement of Pakistan. Renewable energy resources are recyclable springs that can cut the intensity of carbon emissions and encourage to the promising magnitude of FDI and also certify sustainable and economic progression of Pakistan. [ABSTRACT FROM AUTHOR]

This research has examined the dynamic linkages among climate change factors, such as CO2 emissions, temperature, rainfall, and cereal yield in Turkey from 1968 to 2014. At first step, we tested stationary properties of the climatic factors and crop yield by using both traditional and breakpoint unit root tests. After the confirmation of given properties, we used the autoregressive distributed lag (ARDL) model to capture the dynamic relationship among the variables in the given span of time. The empirical results show that there is a long-run equilibrium relationship that exists between climate change factors and cereal yield. CO2 emissions and average temperature have a diverse effect on the cereal yield, whereas average rainfall has a positive effect on the cereal yield in both the long run and short run. To check the causality, we use the Granger causality test that reveals a significant effect of climate change variables on the cereal yield. The unidirectional causal link is significant among temperature and rainfall factors. The results show that the cereal yield is affected by more climate factors like rain fall and temperature due to CO2 emissions as compared to land and labor use. Based on the findings of the study, few suggestions have been made to address the climate change factors. Devise agriculture-specific adaptation policy for the farmers to build their capacity and resilience to tackle climate changes, for example, farm practices. Agriculture research and development should work on cereal crop varieties that can tolerate the high temperature and precipitation. These policies could help the agriculture sector to sustain production and allocation efficiency in the long run. [ABSTRACT FROM AUTHOR]

The current empirical study explores the linkage between carbon dioxide (CO2) emissions, average temperature, cultivated area, consumption of fertilizer, and rice production in Pakistan. For this research, the annual time series data from 1968 to 2014 were used to enhance the validity of the empirical outcomes. The cointegration analysis with the auto-regressive distributed lag (ARDL) bounds testing approach is applied to explore the effects of climate change on rice production. Additionally, the estimated long-run outcomes are verified by employing fully modified ordinary least squared (FMOLS) and canonical cointegrating regression (CCR) approaches. The empirical outcomes revealed that the selected important study variables are cointegrated demonstrating the existence of long-run linkages among them. The main fruitful outcomes of this study are that rice production in Pakistan is positively affected by the carbon dioxide (CO2) emissions in both long-run and short-run. [ABSTRACT FROM AUTHOR]

Energy affects the economic growth and development of a country. Renewable energy has become an important part of the world's energy consumption. The use of fossil fuel energy contributes to global warming and carbon dioxide emissions, and has a detrimental effect on the environment. The long-run and short-run causality relationships between electric power consumption, renewable electricity output, renewable energy consumption, fossil fuel energy consumption, energy use, carbon dioxide emissions, and gross domestic product per capita for Pakistan over the period of 1990–2017 were investigated in this paper using the autoregressive distributed lag bounds testing approach to cointegration. The augmented Dickey–Fuller unit root test and the Phillips–Perron unit root test were used to check the stationarity of the variables, while the Johansen cointegration test was applied to check the robustness of the long-run relationships. The Granger causality test under the vector error correction model extracted during the short-run estimation showed a unidirectional relationship among all variables except for the relationship between gross domestic product per capita and carbon dioxide emission, which was bidirectional (feedback hypothesis). The evidence showed that in the long run, carbon dioxide emissions, electric power consumption, and renewable electricity output had a positive and significant relationship with the gross domestic product per capita, while the relationship of renewable energy consumption, energy use, and fossil fuel energy consumption with the gross domestic product per capita had a negative effect. Overall, the long-run effects of the variables were found to have a stronger effect on the gross domestic product per capita than the short-run dynamics, which indicated that the findings were heterogeneous. The evidence suggests that the government of Pakistan should take steps to enhance the use of renewable energy resources to resolve the energy crisis in the country and introduce new policies to reduce carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Background: This study explores the electricity access to rural and urban populations and its impact on the economic growth in Pakistan.Methods: An autoregressive distributed lag (ARDL) bounds testing approach was applied, and a co-integration test was used to investigate the dynamic causality relationships between the study variables. By using this testing approach, this study filled the literature gap regarding the access of rural and urban population to electricity in Pakistan.Results: The tests shed light on the long-run relationship among the variables, whereas the results revealed that the access of both rural and urban populations to electricity had a positive and significant effect on economic growth.Conclusions: According to these findings, we can conclude that Pakistan should pay further attention to increasing its electricity production from different sources, including not only hydroelectric, solar, wind, oil, gas, and biomass but also fewer and fewer nuclear sources, in order to fulfill the country's demands on its way to a future of sustainable development. [ABSTRACT FROM AUTHOR]

Background: This study examines the access to credit, credit investment, and credit fungibility for small-holder farmers and medium- and large-scale farmers in the agricultural sector of the Shikarpur District of Sindh, Pakistan.Methods: A standardized questionnaire was used to collect data from 87 farmers in the Shikarpur District. We investigated the availability of credit and the use of credit fungibility by farmers with small-, medium-, and large-scale holdings by applying a credit fungibility ratio and an ANOVA technique. The factors that influence the farmers’ access to agricultural credit were analyzed using a probit regression model.Results: The results revealed that farmers in both study groups used some amount of their agricultural credit for non-agricultural activities. Further, the results of the probit regression analysis showed that formal education, farming experience, household size, and farm size had a positive and significant influence on the farmers’ access to agricultural credit.Conclusion: Based on these findings, our study suggests that a strong monitoring of farmers is needed in the study area. [ABSTRACT FROM AUTHOR]