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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 (CO 2 ) 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 CO 2 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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The present study aims to scrutinize the long- and short-run relationship along with the direction of causality among environmental pollution (CO 2 ), 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 (CO 2 ), 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 (CO 2 ). Results also revealed that poverty alleviation, exchange rate, and renewable energy usage substantially negatively influence environmental pollution (CO 2 ). 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)., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 (ECI 2 ) 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., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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 CO 2 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 CO 2 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., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

With the steady growth of CO 2 emissions embedded in trade, the driving forces of emissions have attracted extensive attention. Most of the literature has verified a bundle of the influential factors; however, further analyses are necessary to understand the predominant and heterogeneous driving factors in different economies and/or industries. Accordingly, by applying the multiregional input-output (MRIO) model, this article firstly evaluates the embodied carbon emissions of China's export from 1992 to 2020 in total volumes and by 14 industries. Then, the Least Absolute Shrinkage and Selection Operator (LASSO) estimations allow us to discover that urbanization, technology update and gross domestic product (GDP) are the leading three prioritizing factors in generating China's export emissions. Interestingly, this paper discovers that raising the proportion of female parliamentarians contributes to an abatement of emissions. Furthermore, the empirical results suggest that the heterogeneities of those factors do exist among industries. For example, the percentage of females in parliaments turns out to have a larger effect among labor-intensive industries only. In facing with rapid globalization and economic development of China, this paper provides important policy implications towards specific industries in terms of mitigating trade emissions. It guides policy-makers to achieve "carbon neutrality" by avoiding carbon leakage in net-export countries such as China.

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 CO 2 emissions and improve the environmental quality, while non-renewable energy consumption, economic development, and agricultural output increase the level of CO 2 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 CO 2 emissions. These findings have inherent policy implications for the central and local Chinese government, which are exhibited in the "Conclusions" section., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study assessed the long-run (LR) and short-run (SR) impacts of climatic and non-climatic factors, i.e., CO 2 emissions (CO 2 e), 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 CO 2 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, CO 2 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., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study aims to explore the dynamic association among crop production, livestock production, power consumption in agriculture, forest area, and carbon dioxide (CO 2 ) 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 CO 2 emissions in both cases. However, power consumption in agriculture and forest area has a negative effect on it, indicating that both variables reduce CO 2 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 CO 2 emissions. The causality between livestock production and CO 2 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.

Energy acts as a catalyst to boost the human development index (HDI) in a country. However, the overuse of energy leads to environmental deterioration, which is a byproduct of economic development. Due to the utilization of non-renewable energy sources for a long time, worldwide environmental conditions have become alarming. This study investigates the relationship between renewable and non-renewable energy consumption, economic growth, environmental sustainability, and the human development index (HDI) in Pakistan. The investigation incorporates population growth and technology variables to form a multivariate framework. We use a fully modified ordinary least squares (FMOLS) approach to time-series data from 1990-2017. To check the robustness of estimations, we apply the Gregory-Hansen test with a causality test under the VECM to confirm this association's directions. Our findings confirm that non-renewable energy sources have a positive association with economic growth and CO 2 emissions. However, human development, technology, and renewable energy boost economic development and reduce environmental pollution in Pakistan. The co-integration results confirmed the long run connectivity among all variables. The causality outcomes support the bidirectional causality between renewable and non-renewable energy consumption, economic growth, and CO 2 emissions, both in the short and long run. These outcomes suggest that Pakistan should focus on energy shifts and gradually increase the share of renewables in its energy mix under the China Pakistan Economic Corridor (CPEC). Additionally, the government should increase human and technological development to enhance economic and environmental sustainability.

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.

The current empirical study explores the linkage between carbon dioxide (CO 2 ) 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 (CO 2 ) emissions in both long-run and short-run.

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.

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]

The current study examines the effect of global climate change (CO 2 emissions), financial development, and technical progress (fertilizer consumption and improved seed distribution) on cereal production in Pakistan over the 1977–2014 period. The study undertakes the autoregressive distributed lag (ARDL) bounds testing approach to investigate the long-term interrelation among the variables. The outcomes of the ARDL bounds-testing approach confirmed the presence of a long-term relationship among the variables. Empirical results revealed that CO 2 emissions have a negative impact on cereal production in the short-run and long-run. It means that increase in global climate change will decrease cereal production. Findings further showed that financial development has a positive impact on cereal production in both cases. It suggests that increasing financial development will enhance cereal production, which will ensure the country's food security. In addition, technical progress has a significantly positive impact on cereal production in both cases. The dynamic OLS, fully modified OLS, and the canonical cointegrating regression estimators confirmed the robustness of the findings. Financial development is essential for sustainable agriculture production; therefore, policymakers should devise a comprehensive agriculture policy that addresses the financial needs of the agriculture sector and attracts investment. The financial institutions may use carbon financing as a tool to intervene in the financial market to generate funds for the application of cleaner production principles and the use of best practices for agriculture development and sustainable production. Image 1 • We examined the impact of financial development, climate change, and technical progress on cereal production. • The ARDL approach is employed to explore the long-run relationship among the variables. • Findings reveal that financial development improves cereal production in both long-run and short-run. • CO 2 emissions have adverse impact on cereal production in both cases. • Technical progress will leads to increase cereal production in both long-run and short-run. [ABSTRACT FROM AUTHOR]