Causal relationship between agricultural production and carbon dioxide emissions in selected emerging economies

Fertilizer use and agricultural practices in the paradox of maize crop production and environmental sustainability

The enduring existence of pollution presents a substantial danger to human health, natural systems, and social welfare. Human activities mostly generate greenhouse gas emissions, namely carbon dioxide, which negatively impacts the environment. This study used annual datasets to examine the association between maize crop production, maize yield, fertilizer consumption, agricultural land use, and environmental quality in China. In order to identify the positive and negative shocks with the assessment of short- and long-run dynamics, the study used an asymmetric Nonlinear Autoregressive Distributed Lag (NARDL) approach. A Robust Least Squares method was also used to locate the parameters nexus in order to assess the series' robustness. Results from the long-run interaction indicate that the maize crop production and agricultural land use has a positive impact on CO2 emissions with probability values of (0.000), (0.000), and (0.001), (0.780), respectively, via both positive and negative interruptions. Additionally, maize yield exposed a detrimental effect on environmental quality. Results of the robust least squares analysis showed that maize crop production, fertilizer consumption, and agricultural land use had a positive influence on environmental quality, with probability values of (0.000), (0.004), and (0.949), respectively. However, there is an unfavourable relationship between variable maize yields and CO2 emissions. China should play a significant role in seeking to reduce carbon dioxide emissions and adopt the beneficial policies necessary to ensure the environment's long-term sustainability, since these emissions are now a rising issue around the world.

Exploring the dual impact: Dissecting the impact of tourism agglomeration on low-carbon agriculture

Despite extensive research on the relationship between tourism and agriculture, the specific impact of tourism on agriculture's low-carbon transition has not been thoroughly investigated. This study analyzes the effects of tourism agglomeration on agricultural carbon intensity across 30 Chinese provinces from 2001 to 2020. It is framed within the context of rural digitalization, with a particular emphasis on the integration of agro-tourism and the total factor productivity of agriculture. Utilizing spatial econometric models, we find that tourism agglomeration hinders the low-carbon transition in agriculture by influencing carbon intensity both directly and indirectly. At the national level, the impact of tourism agglomeration follows an inverted-U curve with respect to agro-tourism integration and carbon intensity. At the regional level, the effects vary, with weaker indirect influences in major grain-producing areas. Furthermore, rural digitalization appears to lessen the adverse impacts of tourism on carbon intensity. This study also identifies significant spatial spillover effects from tourism agglomeration. The findings suggest that provinces with high tourist influx should enhance investments in climate-smart agricultural practices and technologies to counteract these negative impacts. Moreover, integrated governance of tourism and agriculture is essential for achieving carbon neutrality in both sectors.

Assessing the impacts of crop production on climate change: An in-depth analysis of long-term determinants and policy implications

This research investigates the long-term determinants of carbon emissions in three diverse regions-Europe and Central Asia (ECA), Sub-Saharan Africa (SSA), and the Middle East and North Africa (MENA)-spanning 1990 to 2020. Utilizing advanced econometric models and analyses, including the Regularized Common Correlated Effects Estimator (rCCE), Common Correlated Effects Estimator (CCE), and Mean-Group (MG) approach, the study explores the intricate relationships between carbon emissions, crop production, emissions per agricultural production, energy consumption, renewable energy consumption, per capita GDP, and population. Region-specific nuances are uncovered, highlighting the varying dynamics: ECA exhibits intricate and non-significant relationships, SSA showcases significant effects of population dynamics and green technology adoption, and the MENA region reveals a nuanced interplay between emissions per agricultural production.The findings underscore the universal efficacy of green technology adoption for mitigation. Strategies for mitigating carbon emissions in the agricultural sector require diversified energy transition approaches, emphasizing efficiency enhancements, green technology adoption, and tailored population management strategies based on regional intricacies. Counterfactual simulations indicate the potential efficacy of strategic measures targeting crop production to reduce carbon emissions, while acknowledging the nuanced relationship between economic growth and emissions. Policymakers are urged to recognize the persistence in emission patterns, emphasizing the importance of targeted interventions to transition towards more sustainable trajectories. Overall, the research provides essential insights for crafting effective policies at both regional and global scales to address the complexities of climate change mitigation in the agricultural sector.

Long-term relationships of beef and dairy cattle and greenhouse gas emissions: Application of co-integrated panel models for Latin America

The cattle sector plays a pivotal role in the economies of numerous Latin American and Caribbean countries. However, it also exerts a significant impact on environmental degradation, including substantial contributions to greenhouse gas emissions (accounting for 23.5 % of global livestock emissions) and deforestation (70 % attributed to livestock in South America). This article aims to investigate the complex, long-term, and short-term relationships between population growth, pastureland expansion, deforestation, and the cattle sector in 15 countries across the region, focusing on their effects on greenhouse gas emissions as well as beef and dairy production. Utilizing data from FAOSTAT spanning the period from 1990 to 2019, a cointegrated panel model was developed using the Pooled Mean Group technique, resulting in the estimation of six models. The aggregate-level results for the region reveal the presence of relatively stable long-term relationships. This implies that over time, the influence of population growth, pastureland expansion, and deforestation on greenhouse gas emissions from cattle production tends to diminish in significance. This long-term behavior may be particularly pronounced in countries with more developed cattle sectors, where efforts to mitigate the environmental impacts of cattle production, such as promoting improved forage technologies, silvo-pastoral systems, grazing management practices, and the implementation of policies, regulatory frameworks, and incentives, have gained traction. These progressive countries can serve as regional benchmarks, and the lessons they have learned hold valuable insights for the sustainable intensification of cattle production in countries with less-developed cattle sectors.

Exploring the effects of climate change and government stability on internal conflicts: evidence from selected sub-Saharan African countries

Climate change has been linked to water scarcity, land degradation, and food insecurity, exacerbating existing tensions and creating new conflicts in countries with weak political institutions. Despite the critical need for effective conflict management and climate adaptation measures, prior studies failed to emphasize the role climate change plays in civil clashes in conflict-affected countries. In this research, we undertake a comprehensive investigation of the effects of climate change and government stability on internal conflicts in 14 selected SSA nations between 1996 and 2016. The study embraces contemporary heterogeneous panel techniques to address heterogeneity and cross-sectional dependence issues that usually appear in panel data estimates. We employed second-generation unit root tests, such as CADF and CIPS, to determine the order of integration of the variables. In addition, Pedroni and Westerlund cointegration tests confirmed the long-run relationship among the variables. Although temperatures were insignificant, the long-run results of the pooled mean group (PMG) approach suggested that civil conflicts decline when precipitation increases. In addition, the outcomes indicate that environmental degradation and population growth are long-run aggravators of social unrest. The short-run results suggest that rising temperatures exacerbate civil conflicts in the selected SSA countries. However, the study found that government stability lessens internal conflicts in the short run, but not in the long run. The DOLS technique validated the long-run outcomes of the PMG technique. Based on the findings of the study, conflict-prone SSA countries should integrate climate change adaptation and conflict prevention strategies, implement sustainable water resource management practices, and endorse climate-related conflict resolution.

Impact of technology on the relation between disaggregated energy consumption and CO2 emission in populous countries of Asia

All around the world, but particularly in developing nations, carbon dioxide emissions are on the rise, and climate change and global warming are brought on by an increase in CO₂ emissions. This article provides an overview of the technological effect on energy consumption in the residential, transport, and industrial sector and its ultimate effect on the environment. Using the STIRPAT-Kaya-EKC model for the years 1990 to 2020, this study looked at the threshold impact of technological advancements on the link between disaggregated energy use and CO₂ emissions for a panel of 10 Asian countries using the panel threshold regression. Findings demonstrate that the EKC phenomenon is present in the chosen Asian region. Findings also suggest that technology has a threshold influence on the relationship between energy use and carbon emissions; however, this effect varies across sectors.

Impact of land consolidation on agricultural decarbonization: Estimation of changes in carbon dioxide emissions due to farm transport

Areas used for agriculture are a large source of carbon emissions, but there is great potential for reducing them. Land consolidation, through the comprehensive reorganization of the spatial arrangement of farms, can reduce emissions as a result of reducing fuel consumption. The subjects of this study are the veracity of this statement and the scope of variation in the potential reduction of carbon emissions. The analysis covered six land consolidation projects in Poland and Turkey, for several agricultural models that differ in the level of fuel consumption. Changes in agricultural road layout resulting from the implementation of land consolidation projects and changes in the number of farm plots and their spatial distribution were considered. The study considered several different levels and structures of fuel consumption on farms. The applied methodology is based on analysis of changes in distance to fields resulting from land consolidation projects, which are then expressed as changes in fuel consumption. The obtained emission reduction results for the studied land consolidation projects were diverse and range from 0.3 to 170 kg CO₂/ha/year. The reduction in fuel consumption on farms at the level of individual villages reached a maximum of 32 %, while the average value of this reduction in the entire surveyed set was 12.5 %. The proposed approach increases the accuracy of existing methods for estimating the long-term balance of carbon emissions and carbon accumulation related to the implementation of land consolidation projects. The observed emission reduction values can be considered a significant economic and ecological effect because the effects of these projects persist for at least several decades.

Deep decarbonization options for the agriculture, forestry, and other land use (AFOLU) sector in Africa: a systematic literature review

Greenhouse gases (GHG) emanating from agriculture, forestry, and other land use (AFOLU) sector are among top contributors to anthropogenic climate change in Africa and globally. Minimizing AFOLU sector GHG emissions in Africa is notoriously hard because of difficulties in emission estimation, the disperse nature of AFOLU emissions, and the complex links between AFOLU activities and poverty reduction. Yet, there are very few systematic reviews dealing with decarbonization pathways for the AFOLU sector in Africa. This article explores the options for achieving deep decarbonization of AFOLU sector in Africa, through a systematic review. Using the method of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA Statement), 46 studies of interest were selected from the databases of Scopus, Google Scholar, and Web of Science. Four sub-themes were identified from the critical review of the selected studies covering key decarbonization approaches used in AFOLU sector. The literature suggests that while forest management and reforestation reduction of GHG in animal production and climate-smart practices in agriculture hold great promises for AFOLU sector decarbonization in Africa, there appears to be very limited coherent policy in the continent addressing any of these AFOLU sub-sectors.

How do carbon emissions, economic growth, population growth, trade openness and employment influence food security? Recent evidence from the East Africa

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.

Modelling the impacts of climate change on cereal crop production in East Africa: evidence from heterogeneous panel cointegration analysis

Climate change has become an issue of concern for sustainable agriculture production. East African nations are heavily reliant on the agriculture sector, which accounts for a substantial amount of their gross domestic product (GDP) and employment. Due to climatic fluctuations, the output of the sector became very unpredictable. Hence, this study investigates the effects of climate change on cereal crop production in nine East African nations between 1990 and 2018. The study implemented pooled mean group (PMG) approach to examine the long-run and short-run dynamic impacts of the varying climatic circumstances on the output of cereal crops. The results reveal that rainfall and carbon emissions have favourable and significant long-run effects on cereal crop output, even though their short-run impacts are negligible. Additionally, cultivated land area and rural population have a constructive role in enhancing agricultural output both in the long-run and short-run. However, average temperatures have negative repercussions on cereal crop production in the long-run and short-run, even though the magnitude of sensitivity is greater in the short-run. Dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS) validated the robustness of the long-run findings of the PMG technique. Besides, the Dumitrescu-Hurlin panel causality outcomes indicate that cereal crop output has a bidirectional causality with temperature, carbon emissions, and cropped area. The study further demonstrated unidirectional causation from rural population to cereal crop yield. The study recommends that East African policymakers improve the quality of farm inputs, the adoption of climate-resilient farming practices, the development of water retention facilities and the establishment of crop diversification initiatives.

An econometric evaluation of the effects of economic growth, energy use, and agricultural value added on carbon dioxide emissions in Vietnam

Global climate change caused by Greenhouse Gases (GHGs), particularly carbon dioxide (CO₂) emissions, poses incomparable threats to the environment, development and sustainability. Vietnam is experiencing continuous economic growth and agricultural advancement, which causes higher energy consumption and CO₂ emissions. Understanding Vietnam’s sensitivity to climate change is becoming more crucial for governments trying to reconcile climate change mitigation and sustainable development. Analyzing pollution-development trade-offs can help minimize environmental degradation in Vietnam. Therefore, the present study empirically investigated the nexus between economic growth, energy use, agricultural added value and CO₂ emissions in Vietnam. To investigate the short-run and long-run relationships between the variables, this study employed the autoregressive distributed lag (ARDL) technique and the Vector Error Correction Model (VECM) using the time series data from 1984 to 2020 for Vietnam. The empirical findings indicated that economic growth and energy use trigger environmental degradation by increasing CO₂ emissions, whereas enhancing agricultural added value improves Vietnam’s environmental quality by reducing CO₂ emissions in both the long-run and short-run. The estimated results are robust compared with alternative estimators such as dynamic ordinary least squares (DOLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). This research contributes to the existing literature by shedding light on the potential of agricultural added value to reduce emissions in Vietnam and provides policy recommendations in areas of low-carbon economy, promoting renewable energy, and sustainable agriculture that can reduce CO₂ emissions in Vietnam.

The roles of energy, natural resources, agriculture and regional integration on CO2 emissions in selected countries of ASEAN: does political constraint matter?

This paper empirically examines the effects of energy, natural resources, agriculture, political constraint and regional integration on CO₂ emissions in four ASEAN (Association of Southeast Asian Nations) countries of Cambodia, Malaysia, Indonesia and Thailand. We distinguish between renewable and fossil fuel energy consumption to see their individual impacts on CO₂ emissions. The study employed a panel data from 1990 to 2019 derived from sources such as World Development Indicators, which were then analysed using Common-Correlated Effect Mean Group (CCEMG) and Augmented Mean Group (AMG) estimates. The findings show that renewable energy consumption has a negative impact on CO₂ emissions while fossil fuel energy degrades the environment. The role of natural resources was found to be favourable for environmental quality with the impact of agriculture being found to be detrimental. For regional trade integration, its influence was not significant enough to offset CO₂ emission. Furthermore, we discovered that political constraint induces CO₂ emission. Based on the result, it is recommended that the selected ASEAN countries promote the use of renewable energy and clean technologies in their manufacturing processes, conserve natural resources, adopt eco-friendly political policies and intensify regional integration to accelerate the achievement of the SDGs.

The relationship between environmental degradation, agricultural crops, and livestock production in Somalia

Climate change is an imminent threat to both developing and developed countries. Various determinants of climate change have been discovered in the literature including, inter alia, the agriculture sector. To this end, this study models the role of agricultural crops - maize, sesame, sorghum, and wheat productions - and livestock production in environmental degradation in Somalia for the period of 1985 to 2017. The study applied the autoregressive distributed lag model (ARDL) for the long-run cointegration between the variables, and vector error correction modeling (VECM) for short- and long-run causalities among the variables. The empirical result revealed the presence of a long-run cointegration between environmental degradation, agricultural crops, and livestock production. All the crops and livestock production increase environmental degradation except wheat production which has a constructive role in reducing environmental degradation in the long run. In contrast, the VECM results detected a short-run causality from sorghum to livestock production. Environmental degradation, sesame, sorghum, and wheat productions cause maize production significantly in the short run as well as in the long run. Moreover, sesame production causes sorghum production in the short run. Likewise, a long-run causality is established from environmental degradation, maize, sesame, livestock, and wheat production to sorghum production. However, Somalia policymakers should institute agricultural policies that are not only sustainable for agricultural production practices to meet the growing food demand but also sustainable to the environment.

The effect of China's leading officials' accountability audit of natural resources policy on provincial agricultural carbon intensities: the mediating role of technological progress

China is one of the largest agricultural countries in the world. In the context of China's efforts to achieve dual carbon goals (carbon peak and carbon neutral), the need for carbon emissions reductions in the agricultural sector cannot be ignored. This study collected 2007 to 2018 data from 30 Chinese provinces and used a difference in differences (DID) model to investigate the relationships between China's leading officials' accountability audit of natural resources policy (LOAANR), agricultural technological progress, and agricultural carbon emissions intensities (CEI). A common trend test, placebo test, PSM-DID, and other test methods were used to verify the reliability of the results. The results show that (1) compared with the non-pilot areas, the policy implementation could significantly reduce CEI; (2) the LOAANR was able to stimulate patented technological progress (ATI) and mechanical technological progress (AMT), but different types of technological progress had different mediation effect sizes; and (3) the policy effects shows obvious regional heterogeneity, manifesting as west > east > central; and the policy effects were more obvious in the non-major grain-producing areas, but had no inhibition effects on the CEI in the major grain-producing areas; compared with low intensity market-based environmental regulation (MER) regions, high-intensity MER regions have stronger LOAANR emission reduction effects. Based on the study findings, policy suggestions are given to reduce agricultural carbon emissions and promote higher quality agricultural development.

The Carbon Emission Characteristics and Reduction Potential in Developing Areas: Case Study from Anhui Province, China

Global warming and world-wide climate change caused by increasing carbon emissions have attracted a widespread public attention, while anthropogenic activities account for most of these problems generated in the social economy. In order to comprehensively measure the levels of carbon emissions and carbon sinks in Anhui Province, the study adopted some specific carbon accounting methods to analyze and explore datasets from the following suggested five carbon emission sources of energy consumption, food consumption, cultivated land, ruminants and waste, and three carbon sink sources of forest, grassland and crops to compile the carbon emission inventory in Anhui Province. Based on the compiled carbon emission inventory, carbon emissions and carbon sink capacity were calculated from 2000 to 2019 in Anhui Province, China. Combined with ridge regression and scenario analysis, the STIRPAT model was used to evaluate and predict the regional carbon emission from 2020 to 2040 to explore the provincial low-carbon development pathways, and carbon emissions of various industrial sectors were systematically compared and analyzed. Results showed that carbon emissions increased rapidly from 2000 to 2019 and regional energy consumption was the primary source of carbon emissions in Anhui Province. There were significant differences found in the increasing carbon emissions among various industries. The consumption proportion of coal in the provincial energy consumption continued to decline, while the consumption of oil and electricity proceeded to increase. Furthermore, there were significant differences among different urban and rural energy structures, and the carbon emissions from waste incineration were increasing. Additionally, there is an inverted "U"-shape curve of correlation between carbon emission and economic development in line with the environmental Kuznets curve, whereas it indicated a "positive U"-shaped curve of correlation between carbon emission and urbanization rate. The local government should strengthen environmental governance, actively promote industrial transformation, and increase the proportion of clean energy in the energy production and consumption structures in Anhui Province. These also suggested a great potential of emission reduction with carbon sink in Anhui Province.

Does sectoral modernization promote CO2 emissions? Dynamic panel analysis of selected Asian countries

Modernization refers to the general process of progress within human societies. As a result of carbon dioxide emissions, global warming poses a significant threat to human sustainability. Therefore, the scientific community must thoroughly research the impact of modernization on CO₂ emissions. Surprisingly, just a few pieces of research have previously examined this subject. Asian economies are regarded as laggards in achieving the Sustainable Development Goals (SDGs), with environmental degradation being one of the key issues confronting these countries. This study used panel data regression to investigate the association between sectoral modernization and CO₂ emissions in South Asia. None of the existing studies has observed the sectoral modernization and CO₂ emission nexus in the region of South Asia. Therefore, this study bridges the research gap in the current literature. This study found that social sector transformation, energy sector, and services sector modernization have negative associations with CO₂ emissions in South Asia. However, both the agricultural sector and industrial sector modernization are positively associated with CO₂ emissions in South Asia. The empirical results from several models suggest that sectorial modernization has a significant and strong association with environmental sustainability. Access to a green and eco-friendly economy can increase modernization among different sectors of the economy to produce pro-environment products and society. Also, we found that FDI and trade policies influence the economies to follow environment-friendly economic practices.

Empirical analysis of rice and maize production under climate change in China

The past few decades of extreme drought and flooding caused by changing climate conditions have significantly affected agricultural production globally. This study focuses on two vital crops in China-maize and rice-and provides a comprehensive analysis of how these crops are affected by climate change-induced factors over the periods 1978Q1-2015Q4. Four key findings were obtained. First, using a nonparametric approach to estimate actual and observed trends of climatic variables, the results show a significant positive trend in average temperature from February to October. On the other hand, seasonal temperature increases during spring, summer, and autumn. Second, the results show no significant change in the monthly, seasonal, and annual rainfall patterns when examined over the study period. Third, using an autoregressive distributed lag (ARDL) model, we find that while temperature and rainfall do not significantly support rice production in the long and short run, they play a substantial role in maize production in China. Finally, we find no significant difference in the results for rice when the quantile regression (QR) technique that controls for distributional asymmetry effects is employed. However, the impact of temperature on maize decreases at higher quantiles. Given the outcomes of our study, we argue that an advanced irrigation system is crucial and must be encouraged to minimize the effects of climate change on crop production.

Influence of External Thermal Conditions on Temperature–Humidity Parameters of Indoor Air in a Czech Dairy Farm during the Summer

Simple Summary

The creation and provision of a suitable indoor environment for animals in dairy farms has become increasingly important in recent years, especially in the summer. Greater attention is paid mainly to lactating dairy cows. This research shows that great attention should be paid not only to cowsheds for lactating dairy cows but also to the housing facilities for other categories of cattle kept on farms. In this article, the basic parameters of the thermal state of the environment during the summer period are assessed regarding the housing facilities. The analysis shows that more attention needs to be paid to the housing of calves. In the facilities for calves, the values of the thermal state of the environment were at an extremely dangerous level, especially the high air temperatures, which exceed the recommended limit values.

Abstract

The aim of this article is to show the relationship between external thermal conditions and the quality of the indoor environment on a dairy farm during the summer. The measurements were carried out on a large dairy farm of Holstein cattle situated in the Czech Republic. The research included the measurement of the cowshed for 440 lactating cows, a milking parlor, a maternity cowshed, a cowshed for dry cows, 69 individual calf hutches, and three outdoor group shelters for calves. The results of the registration measurements of the thermal state parameters outside and inside the buildings were analyzed. The critical and dangerous situations were especially regarding the calves. The highest temperature in the calf hutches was 48 °C with the value of THI_max = 90.1, while in the calf group shelters it was 46.9 °C with the value of THI_max = 89.4. The research results showed that not only the critical values of temperature and the temperature–humidity index that affect the housed animals are important but also the duration for which the animals are exposed to heat stress. The massive masonry constructions of the milking parlor and also of the cowshed for dry cows dampened the temperature rise in these barns, with good values for the attenuation coefficient.

Changes in Energy-Related Carbon Dioxide Emissions of the Agricultural Sector in Poland from 2000 to 2019

This paper analyzes the changes in carbon dioxide (CO2) emissions related to energy consumption in the Polish agricultural sector between 2000 and 2019. Based on the Logarithmic Mean Divisia Index (LMDI), the changes in agricultural CO2 emissions are viewed in the context of changes in six factors, i.e., CO2 emission intensity, substitution of fossil fuels, penetration of renewable energies, energy intensity, labor productivity and number of employees. The analysis demonstrated that total energy consumption declined over the study period; this was related to a reduction in the intake of energy derived from solid fossil fuels (−1.05%), crude oil (−1.01%), electricity (−4.89%), and heat (−1.37%), and to an increased consumption of natural gas (5.78%) and biofuels (0.82%). Furthermore, it follows from the analysis that changes in CO2 emissions witnessed in that period were consistent with changes in energy consumption levels; this resulted from a negligible transformation of the energy mix (largely determined by fossil fuels). Generally, CO2 emissions declined over the study period at a rate comparable (−0.9%) to that of the reduction in energy consumption (−1.03%). In light of the LMDI method, the reduction in CO2 emissions from fuel consumption in the Polish agricultural sector was mainly driven by a reduction in energy intensity and in employment. Conversely, rapid growth in labor productivity was the key factor in increasing carbon dioxide emissions. Compared to these impacts, changes in other factors (i.e., emission intensity, energy mix and penetration of renewable energies) had an extremely small or marginal effect on the variation in CO2 emissions.

More Is More? The Inquiry of Reducing Greenhouse Gas Emissions in the Upstream Petroleum Fields of Indonesia

Global dependence on fossil fuels remains high despite the rapid expansion of renewable energy initiatives, and fossil fuels extracted from the earth’s crust are major contributors to greenhouse gasses. Unlike greenhouse gas studies in the downstream area, currently, few studies have investigated greenhouse gas in the upstream field, and there is no published study related to carbon emission influencing factors in Indonesia’s upstream field. A short panel data analysis is used to investigate the influence of oil and gas production and energy usage on greenhouse gas emissions by using data from 25 upstream fields (including offshore and onshore fields) collected from 2015 to 2018. The results show that maintaining a constant energy usage leads to increased oil and gas production and reduced greenhouse gas emissions. This pattern implicitly indicates that improving energy efficiency during oil and gas production is critical for ensuring production stability and further reducing greenhouse gas. This study may contribute significantly toward the industrial decarbonization approach that includes upstream processes to achieve net-zero carbon emissions. We recommend further research to study the carbon mitigation pattern in the upstream petroleum fields.

Do Agriculture Technologies Influence Carbon Emissions in Pakistan? Evidence based on ARDL technique

Pakistan is an agrarian country, and the usage of agriculture technologies has increased in this country over the period of time. Extensive use of agriculture technologies may have detrimental impact of environment quality through an increase in carbon dioxide emissions. This study examines the impact of agriculture technologies on carbon emissions in Pakistan by using the annual time series data for the period 1973-2018. For long-run and short-run analysis, autoregressive distributed lag model is applied and the results reveal that cointegration exists among the variables. Long-run results show a significant positive impact of pesticide and economic growth on carbon emission, whereas short-run results confirm the positive effect of economic growth on carbon emissions in Pakistan. This study has important policy implications, such as to increase sustainable economic growth through agriculture sector; there is a need to introduce green technologies that produce less carbon emissions.

Tracking the effect of climatic and non-climatic elements on rice production in Pakistan using the ARDL approach

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.

A district-level analysis for measuring the effects of climate change on production of agricultural crops, i.e., wheat and paddy: evidence from India

The present study aims to examine the impact of climate change on wheat and rice yield in Punjab, India, during 1981-2017. The study employs fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooled mean group (PMG) approaches. The Pedroni cointegration has established a long-run relationship of climate variables with rice and wheat crops. FMOLS and DOLS models show that minimum temperature has a positive effect on both wheat and rice. In contrast, the maximum temperature is found to be negatively contributing to both crops. Rainfall has a significant adverse impact on the production of wheat. In the study period, seasonal rainfall has been found detrimental for the production of wheat and rice crops, indicating that excess rainfall proved counterproductive. Moreover, the Dumitrescu-Hurlin causality test has revealed a unidirectional causality running from minimum temperature, rainfall, and maximum temperature for rice and wheat production. The findings of the study suggest that the government should invest in developing stress-tolerant varieties of wheat and rice, managing crop residuals to curb other environmental effects, and sustaining natural resources for ensuring food security.

Does agriculture-induced environmental Kuznets curve exist in developing countries?

Even though the economic growth and the increased agricultural production bring welfare increment, they could also load significant environmental costs to society. This study aims to investigate the existence of agriculture-induced EKC hypothesis in 47 developing countries during 1976-2017 by using dynamic panel data estimators. According to the long-run findings of the DOLS and the FMOLS estimators, there is an inverted U-shaped relationship that exists between income and the CO₂ emissions, while agricultural production has a negative impact on environmental quality. In addition, the PMG estimator is also employed for robustness check. Likewise the DOLS and the FMOLS findings, the long-run results of the PMG estimator also support the existence of agriculture-induced EKC for developing countries. The empirical findings of this study provide stimulus results of policy makers to re-consider their current production infrastructure of agricultural sector to achieve sustainable development.

How climate change is impacting the major yield crops of Pakistan? an exploration from long- and short-run estimation

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.

Using an asymmetrical technique to assess the impacts of CO2 emissions on agricultural fruits in Pakistan

Human activities such as deforestation and cultivation contribute to the accumulation of carbon dioxide in the environment. Methane is emitted by energy exploration, coal mining, natural gas spills, waste, and waste dumps. Methane generated by such greenhouse gases has significantly contributed to the climate change and global warming. However, the most significant contributor to climate change and global warming is the use of fossil fuels. These fuels contribute to ozone depletion and global warming by emitting greenhouse gases into the atmosphere. The current study key objective was to determine the CO₂ emission effect to key fruit production in Pakistan by taking time series annual data varies from 1970 to 2019. A non-linear autoregressive distributed lag model (NARDL) was employed to check the variables linkages. The consequences of short- and long-run estimates expose that the positive and adverse shocks of citrus fruit create expressively to upsurge the CO₂ emission. Similarly, the positive and negative shocks of mango fruit expose an adverse interaction to CO₂ emission. The positive shock of apple fruit has constructive but negative shock expose and adverse linkage to CO₂ emission. Further, banana fruit also exposes a negative but constructive via positive shock linkage to CO₂ emission. Moving towards the apricot fruit production that exposed a constructive linkage via positive and negative shocks to CO₂ emission. The almond fruit productivity via positive and negative shocks exposed an adverse relation to carbon dioxide emission. Grape fruit via positive shock shows a construct, but negative shock exposed an adverse association to carbon emission in Pakistan. Finally the guava fruit production exposed a construct linkage to CO₂ emission via positive and negative shocks. Since agricultural activities and CO₂ emissions are essential to resolving the pollution problem, a series of practicable steps must be taken by the Pakistani authorities to tackle this issue.

Do international collaborations in environmental-related technology development in the U.S. pay off in combating carbon dioxide emissions? Role of domestic environmental innovation, renewable energy consumption, and trade openness

Many economies are seeking new ways to improve environmental quality through international collaboration in environmental-related technology development (ICERTD). Cost reduction, green market penetration, and green technology development are central to global partnerships for sustainable development, even though no empirical study explains the ICERTD-carbon dioxide (CO₂) emissions nexus. The paper fills this knowledge gap in the environmental economics literature by examining the relationship between ICERTD and CO₂ emissions in the U.S. from 1990Q1 to 2018Q4 using domestic environmental innovation, trade openness, renewable energy consumption, and gross domestic product per capita as control variables. Fully modified ordinary least squares, dynamic ordinary least squares, and correlated component regression methods were employed for testing the long-run nexus among the variables. The present study revealed that (i) a long-run cointegration existed among ICERTD, domestic environmental innovation, trade openness, renewable energy consumption, gross domestic product per capita, and CO₂ emissions; (ii) ICERTD, domestic environmental innovation, and renewable energy consumption benefited the U.S. in lowering CO₂ emissions in the long run; and (iii) trade openness and gross domestic product per capita were positively associated with CO₂ emissions. This study recommends important policy recommendations for increasing ICERTD for decarbonization.

Modeling the impact of climatic and non-climatic factors on cereal production: evidence from Indian agricultural sector

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.

The Impact of Climate Variability and Change on Food Security in Sub-Saharan Africa: Perspective from Panel Data Analysis

This study investigates the relationship between climate variables such as rainfall amount, temperature, and carbon dioxide (CO2) emission and the triple dimension of food security (availability, accessibility, and utilization) in a panel of 25 sub-Saharan African countries from 1985 to 2018. After testing for cross-sectional dependence, unit root and cointegration, the study estimated the pool mean group (PMG) panel autoregressive distributed lag (ARDL). The empirical outcome revealed that rainfall had a significantly positive effect on food availability, accessibility, and utilization in the long run. In contrast, temperature was harmful to food availability and accessibility and had no impact on food utilization. Lastly, CO2 emission positively impacted food availability and accessibility but did not affect food utilization. The study took a step further by integrating some additional variables and performed the panel fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) regression to ensure the robustness of the preceding PMG results. The control variables yielded meaningful results in most cases, so did the FMOLS and DOLS regression. The Granger causality test was conducted to determine the causal link, if any, among the variables. There was evidence of a short-run causal relationship between food availability and CO2 emission. Food accessibility exhibited a causal association with temperature, whereas food utilization was strongly connected with temperature. CO2 emission was linked to rainfall. Lastly, a bidirectional causal link was found between rainfall and temperature. Recommendations to the national, sub-regional, and regional policymakers are addressed and discussed.

Climate change and major crop production: evidence from Pakistan

Climatic changes are posing serious threats to crop production and food insecurity across the globe. This study explores the dynamic relationship between changing annual temperature and production of major crops such as wheat, rice, bajra, jowar, maize, barley, gram, sugar cane, mastered oil, and cotton in Pakistan from 2000 to 2019 through an eclectic production model. The estimated result of panel econometric analysis revealed a significant negative effect of rising temperature on selected crop production in the long run with an insignificant impact in the short run. Among other explanatory variables, the area under cultivation and fertilizer input have significant positive effects in both the long run and the short run. Improved quality seeds revealed insignificant effects and urging authorities to enhance quality research to develop climate change resilient crops. This study urges Pakistan to improve agriculture technology along with adopting other greenhouse gas mitigation, such as forestation and clean energy, and water conservation policies.

Revisiting the energy-economy-environment relationships for attaining environmental sustainability: evidence from Belt and Road Initiative countries

The Belt and Road Initiative (BRI) is an ambitious development project initiated by the Chinese government to foster economic progress worldwide. In this regard, this study aims to investigate the dynamics of energy, economy, and environment among 42 BRI developing countries using an annual frequency panel dataset from 1995 to 2019. The major findings from the econometric analyses revealed that higher levels of energy consumption, economic growth, population growth rate, and FDI inflows exhibit adverse environmental consequences by boosting the CO₂ emission figures of the selected developing BRI member nations. However, it is interesting to observe that exploiting renewable energy sources, which are relatively cleaner compared to the traditionally-consumed fossil fuels, and fostering agricultural sector development can significantly improve environmental well-being by curbing the emission levels further. On the other hand, financial development is found to be ineffective in explaining the variations in the CO₂ emission figures of the selected countries. Besides, the causality analysis shows that higher energy consumption, FDI inflows, and agricultural development cause environmental pollution by boosting CO₂ emissions. However, economic growth, technology development, financial progress, and renewable energy consumption are evidenced to exhibit bidirectional causal associations with CO₂ emissions. In line with these findings, several relevant policies can be recommended for the BRI to be environmentally sustainable.

Short and long-run causal effects of agriculture, forestry, and other land use on greenhouse gas emissions: evidence from China using VECM approach

Climate change caused by different anthropogenic activities is a subject of attention globally. There is a concern on how to maintain a clean environment and at the same time achieve optimal use of land. To this end, this study examines the causal effects of land use including agricultural, forestry, and other land categories on greenhouse gas (GHG) emissions. The data for China is collected over the period 1990 to 2012 for the empirical examination. By employing vector error correction model (VECM), it is found that there is significant long-run causality among variables. However, in the short run expectedly, only land under agriculture has strong causality with the GHG emissions. The results in case of variance decomposition analysis highlight that land under agriculture and other use significantly causes the GHG emissions in the long run. Further, impulse responses of variables are also measured with the Cholesky one standard deviation. The results are robust and support the argument that different land uses cause GHG emissions in China. The study provides insights for policy makers to improve the activities occurring on agricultural and other land uses. Assessment of overall potential, including bio energy, needs to include analysis of trade-offs and feedbacks with land-use competition. Many positive linkages with sustainable development and with adaptation exist but are case and site specific as they depend on scale, scope, and pace of implementation.

The environmental aspects of agriculture, merchandize, share, and export value-added calibrations in Turkey

The intricacy associated with policy design for environmental sustainability has necessitated a reconsideration of the output and environmental degradation relationship. Like many economies across the globe, the sector contributions to environmental woe are likely contingent on the respective economic performance of the sectors. From this perspective, this study examines the environmental effects of the contributions of agriculture value-added, merchandize value-added, export value-added, and share value-added over the period 1991-2019. By employing a combination of econometric techniques, the result revealed that agriculture value-added and export value-added mitigate environmental hazards, while a 1% increase in total energy utilization, merchandize value-added, and share value-added induce carbon emission by about 0.6%, 0.02%, and 0.001%. Moreover, the environmental Kuznets curve (EKC) hypothesis is validated for agriculture value-added and carbon emission nexus. However, there is a significant U-shaped relationship between carbon emission and economic contributions from the merchandize value-added, export value-added, and share value-added, thus suggesting that the EKC hypothesis is not valid. The study suggests that Turkey's agricultural sector is possibly living to the expectation of adopting and incorporating environmental sustainability practices. On the other hand, sustainable environmental policies related to other sectors of the economy are proffered in consonance with the indicated result from the study.

Addressing the long- and short-run effects of climate change on major food crops production in Turkey

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.

Evaluation of energy use and carbon dioxide emissions from the consumption of fossil fuels and agricultural chemicals for paste tomato cultivation in the Bursa region of Turkey

This study was aimed to determine the fossil fuel consumption, energy use, and carbon dioxide (CO₂) emissions in per unit production area (ha) considering the petroleum products (PP) directly used and the chemical fertilizers and pesticides for the cultivation of paste tomatoes in open-field conditions in Bursa region of Turkey. The primary data of the study consisted of data collected by making face-to-face surveys with the producers of paste tomatoes in the Bursa region. The direct energy inputs and CO₂ emissions related to diesel fuel and lubricant oil consumptions of engines of agricultural tractors for cultivation operations and the indirect energy inputs and CO₂ emissions related to the manufacturing of chemical fertilizers and plant growth regulators used for plant nutrition and pesticides used for plant protection were determined for paste tomato cultivation. A total of 288.6 L diesel fuel and 0.067 L lubrication oil are consumed per hectare when using tools and machinery in paste tomato production. A total of 408 kg of chemical fertilizers and 15.5 kg of pesticides are used per hectare in paste tomato production in the Bursa region of Turkey. A total of 2343.45 MJ/ha and 2700.5 MJ/ha indirect energy is used in the application of chemical fertilizers and pesticides, respectively. A total of 792.43 kg and 0.189 kg CO₂ is released as a result of diesel fuel and lubricant oil consumptions. For the production of one kilogram of paste tomato, 2.68 grams (g) diesel fuel and lubricating oil, 175.02 kilojoules (kJ) of energy is consumed, and 15.88 g CO₂ is released in the Bursa region of Turkey.

Investigation on the main contributors of economic growth in a dynamic heterogeneous panel data (DHPD) in Africa: evidence from their income classification

In investigating the key contributors (electricity consumption, foreign direct investment, carbon dioxide emissions, and population) of economic growth in Africa, this study clustered the selected countries into their income levels spanning from 1990 to 2018. Applying the Westerlund bootstrap co-integration unveiled, the employed variables have a long-run equilibrium association. Estimates from the dynamic common corrected effects revealed that a 1% rise in electricity consumption increases economic growth by 0.187%, 0.040%, and 0.511% in upper middle income, lower middle income, and low middle income, respectively. The elasticity of carbon dioxide emissions to economic growth is high in low-income countries than in the other two groupings. In contrast, a percentage rise in foreign direct investment heightened economic growth by 0.919% and 0.154% in upper middle income and lower middle income. As the growth hypothesis was established among the panel groupings, it points out that a country's economy is energy dependent. Thus, a rise in electricity consumption in Africa will lead to a surge in economic growth since energy usage is a direct input into the manufacturing process and/or an indirect input that complements labor and capital inputs. However, its ripple effects of polluting the environment need not be overlooked. These findings imply that electricity usage and economic growth are highly corrected. These approaches consider cross-sectional reliance into consideration; thus, the empirical findings have drawn some significant policy implications.

Agricultural production and CO2 emissions from two sources in the ECOWAS region: New insights from quantile regression and decomposition analysis

Agriculture being the dominant economic activity of the West African economies is responsible for the most greenhouse gasses emitted in the region. Are there heterogeneous determinants of environmental degradation across low, intermediate, and high CO2 emitters in West Africa? Considering the significance of agriculture, industrial activities, renewable energy consumption and economic growth in West-Africa, this paper investigates the conditional determinants of environmental degradation from two sources (per-capita CO2 emission and CO2 emission from liquid sources) using panel data from 15 ECOWAS countries for the period 1990-2015. The study adopts a panel quantile regression technique with non-additive fixed effects as well as quintile decomposition techniques to explore if the relationship between agricultural and economic factors differs across low, intermediate, and high CO2 emitters and the extent of CO2 emission gap between Low Income Group (LIG) and Lower-Middle Income Groups (LmIG). Results from the mean estimators show that while agricultural production impedes CO₂ emissions from liquid sources, it however increases total emissions implying a shift from mechanized farming to more traditional farming methods and the burning and use of biomass from agricultural produce as an energy source. Estimates of the conditional determinants of environmental degradation vary along the quantiles signifying heterogeneity of the determinants of environmental degradation across, low, intermediate, and high CO2 emitters. Additionally, results emanating from the quantile decomposition procedure show that lower-income West African economies have superseded their lower-middle income counterparts at higher quantiles of CO₂ emissions.

Impact of increasing temperature anomalies and carbon dioxide emissions on wheat production

Climate change is one of the serious issues humankind is currently facing. It impacts almost all the processes in nature and threatens the existence of species and biodiversity; hence, the whole process of the food cycle. To mitigate the influence of climate change on vital processes in nature, we need to understand the pattern and magnitude of the relationship between climate change and impacted processes in nature. In this article, we explore the impact of climate change on wheat production in terms of short and long-run relationships between world wheat production, carbon dioxide emissions, and surface temperature anomalies. We present new information on the nexus between climate change and wheat production using autoregressive distributed lag (ARDL) models and ARDL bounds test of cointegration. We observe a significant cointegration relationship among world wheat production, carbon dioxide emissions, and surface temperature anomalies series. Lagged short-run impacts of temperature anomalies and carbon dioxide emissions are found significant. The long-run impact of both series on world wheat production is significant with a high correction speed to any instability between wheat production and the proxies of climate change.

Does Chinese FDI, Climate Change, and CO2 Emissions Stimulate Agricultural Productivity? An Empirical Evidence from Pakistan

Pakistan’s agricultural sector growth is dwindling from the last several years due to insufficient foreign direct investment (FDI) and a drastic climate change-induced raise in temperature, which are severely affecting agricultural production. The FDI has paramount importance for the economy of developing countries as well as the improvement of agricultural production. Based on the time series data from 1984 to 2017, this paper aims to highlight the present situation of the agriculture sector of Pakistan and empirically analyze the short-run and long-run impact of Chinese foreign direct investment (CFDI), climate change, and CO2 emissions on agricultural productivity and causality among the variables. The Autoregressive Distributed Lag Model (ARDL) model and Granger Causality test were employed to find out the long-run, short-run, and causal relationships among the variables of interest. Furthermore, we have employed the Error Correction Model (ECM) to know the convergence of the equilibrium path. The bound test results verified the existence of a long-run association, and the empirical findings confirmed that Chinese FDI has a significant and positive impact, while climate change and CO2 emissions has negative impact on the agricultural growth of Pakistan both in the short-run and long-run. Granger Causality test results revealed that variables of interest exhibit bi-directional and uni-directional causality. The sector-wise flow of FDI reveals that the agriculture sector of Pakistan has comparatively received a less amount of FDI than other sectors of the economy. Based on the findings, it was suggested to the Government of Pakistan and policymakers to induce more FDI in the agriculture sector. Such policies would be helpful for the progress of the agriculture sector as well as for the economic growth of Pakistan.

The nexus of environmental sustainability and agro-economic performance of Sub-Saharan African countries

The increasing concern of environmental degradation and climate change impacts of agricultural-based activities are becoming more pronounced in the Sub-Sahara region of Africa especially due to urgent drive to meeting food, healthy diet, and economic needs. In retrospect. This novel study explores the relationship between agro-economic performance, the Real Gross Domestic Product (GDP), Total natural rent, urbanization and environmental degradation vis-à-vis (Carbon dioxide emissions) in a carbon function. The empirical analysis used a panel data for the period 1980-2014 for the selected countries in sub-Saharan Africa. The Kao test uncovers a cointegration between carbon dioxide emissions, Real Gross domestic product, Total natural rent, agriculture and urbanization. The panel Pooled Mean Autoregressive distributed lag model (PMG-ARDL) posits a positive and significant connection between the gross domestic product and CO2 emissions in the long run. Our examination asserts that agricultural value-added reduces emissions in sub-Saharan Africa while urbanization and natural resource rent both increases CO2 emissions in the long run. In addition, the causality analysis reveals a bidirectional link between agriculture value-added and CO2 emissions. Essentially, policymakers in African nations must pay close attention to the issues of rural-urban drift as this leads to more emissions.

Dynamic relationship among agriculture-energy-forestry and carbon dioxide (CO2) emissions: empirical evidence from China

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.

Climate change and cotton production: an empirical investigation of Pakistan

This study investigates the relationship between climate change, the area under cultivation, fertilizer consumption, and cotton production in Pakistan from 1980 to 2018. The existence and nature of the short-term and long-term relationships are explored by using an autoregressive distributed lag (ARDL) model bounds testing approach. The estimated result of the ARDL bounds testing approach has shown the presence of cointegration between dependent and explanatory variables. The long-term estimates have revealed that the increasing average temperature has a positive insignificant effect, which implies that rising temperature is not increasing cotton yield in Pakistan. The findings of the area under cultivation and fertilizer consumption have revealed significant positive effects in both the long run and short run. This study urges Pakistan to reduce the pace of climate changes and increase water conservation by planting forests and constructing dams across major rivers along with the adoption of environmentally friendly production techniques and inputs.

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.

Empirical analysis of climate change factors affecting cereal yield: evidence from Turkey

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.

Examining the effects of climate change on rice production: case study of Pakistan

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.

Examining the effects of climate change on rice production: case study of Pakistan

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.

Soil CO2 emissions from summer maize fields under deficit irrigation

Irrigation practice is one of the main factors affecting soil carbon dioxide (CO₂) emission from croplands and therefore on global warming. As a water-saving irrigation practice, the deficit irrigation has been widely used in summer maize fields and is expected to adapt to the shortage of water resources in Northwest China. In this study, we examined the impacts of deficit irrigation practices on soil CO₂ emissions through a plot experiment with different irrigation regimes in a summer maize field in Northwest China. The irrigation regimes consisted of three irrigation treatments: deficit irrigation treatments (T1: reduce the irrigation amount by 20%, T2: reduce the irrigation amount by 40%) and full irrigation (T0) treatments. The results showed that the soil CO₂ cumulative emissions with T1 and T2 were decreased by 9.8% (p < 0.05) and 14.3% (p < 0.05), respectively, compared with T0 treatment (1365.3 kg-C ha^-1). However, there were no significant differences between T1 and T2 treatments (p > 0.05). Soil CO₂ fluxes with different irrigation treatments showed significant correlations with soil moisture (p < 0.001) and soil temperature (p < 0.05). It was also observed that summer maize yields with T1 and T2 treatments were reduced by 4.9% (p > 0.05) and 30.9% (p < 0.05), compared with T0 (34.3 t ha^-1), respectively. The findings demonstrate that the deficit irrigation treatment (T1) resulted in a considerable decrease in soil CO₂ emissions without impacting the summer maize yields significantly. The results could be interpreted to develop better irrigation management practices aiming at reducing soil CO₂ emissions, saving water, and ensuring crop yield in the summer maize fields in Northwest China.

Agricultural waste

The management of agricultural waste has become very important because the inappropriate disposal yields negative effects on the environment. The resource recovery from agricultural waste which converts waste into available resources can reduce the waste and new resource consumption. This review summarizes the 2018 researches of over three hundred scholar papers from several aspects: agricultural waste, and, waste chemical characterization, agricultural waste material, adsorption, waste energy, composting, waste biogas, agricultural waste management, and others.