How does full-cost insurance for wheat affect pesticide use? From the perspective of the differentiation of farmers' production scale.

Theoretically, agricultural insurance influences farmers' use of pesticides by changing the expected income of agricultural production. Full-cost insurance, with high guarantee and high compensation characteristics, may significantly affect farmers' pesticide use. First, this paper constructs a production function to characterize and compare the marginal incomes of insured and uninsured farmers under risk uncertainty and analyses how insured farmers can increase marginal income by increasing or reducing factor inputs. Considering scale differentiation, it discusses pesticide use strategies different types of farmers may adopt to maximize household utility. Second, using survey data of the pilot counties of full-cost insurance for wheat in Henan Province, China, the simultaneous equation model is used for empirical testing. The results reveal the following: (i) Farmers' insurance participation and pesticide application behaviour are not mutually independent. (ii) For the whole sample, full-cost insurance for wheat has a significant pesticide reduction effect. (iii) However, considering scale differentiation, pesticide application decreases significantly among insured ordinary farmers but does not change significantly among insured large-scale farmers. Third, policy measures are proposed to activate the green development function of agricultural insurance.

Perceived Value of Information Attributes: Accounting for Consumer Heterogeneous Preference and Valuation for Traceable Agri-Food.

Information attributes characterize traceable agri-food. The perceived value of information attributes influences consumers' preferences for traceable agri-food, consisting of two dimensions, predictive value and confidence value. We examine heterogeneous preferences and willingness to pay (WTP) in China's traceable agri-food market. Using the choice experiments, we explore how the traceability information, certification type, region of origin, and price influence Chinese consumers' Fuji apple choices. We identify three consumer classes by a latent class model: certification-oriented class (65.8%), price-sensitive and origin-oriented class (15.0%), and no-buy class (19.2%). The results show that consumer sociodemographic characteristics, predictive value, and confidence value are the heterogeneous sources that determine their preferences for Fuji apple information attributes. Specifically, consumers' age, family income per month, and whether the family has children under 18 significantly impact the membership probability of consumers in both certification-oriented and price-sensitive and origin-oriented classes. Consumers' predicted value and confidence value significantly impact the membership probability of consumers in the certification-oriented class. In contrast, consumers' predicted value and confidence value have no significant impact on the membership probability of consumers in price-sensitive and origin-oriented class.

Globalization and renewable energy use: how are they contributing to upsurge the CO2 emissions? A global perspective.

The present study major aim was to examine the impact of globalization, economic growth, population growth, renewable energy usage and nuclear energy on CO2 emissions globally by taking the annual data varies from 1985 to 2020. Stationarity among study variables were tested via unit root testing, while nonlinear autoregressive distributed lag (NARDL) technique was used to demonstrate the linkages among variables with the estimation of long-run and short-run. Study results reveal that both in the short run and long run, negative globalization and economic growth shocks positively and negatively influence CO2 emissions, respectively. Besides, higher population growth is found to positively influence CO2 emissions while renewable energy consumption cannot influence the CO2 emission figures. Lastly, positive and negative shocks to alternative nuclear energy consumption are evidenced to negatively influence CO2 emissions both in the short run and long run. Hence, in line with these findings, several new policies and strategies are recommended for reducing carbon emissions globally.

Do Past Experience and Group Heterogeneity Matter to Consumer Preferences? Evidence From a Choice Experiment in Urban China.

This study uses a discrete choice experiment to examine consumers' preferences for Fuji apple product attributes and willingness to pay (WTP) estimates for consumers in six cities in China. We estimated the preference heterogeneity by linking the stated preference choice data with consumers' past experience and socioeconomic characteristics in the latent class model. The empirical results show that, first, the past experience variables are crucial in explaining consumer preferences and WTP. Second, three classes, namely, certification-oriented, price- and origin-oriented, and not interested, are identified. Furthermore, the same type of Fuji apple attribute does not appeal to every respondent. Third, our results indicate the heterogeneity of preferences across different classes of respondents, as well as differences in WTP for Fuji apples.

The Economic Impact of Climate Change on Wheat and Maize Yields in the North China Plain.

Climate change has significantly affected agricultural production. As one of China's most important agricultural production regions, the North China Plain (NCP) is subject to climate change. This paper examines the influence of climate change on the wheat and maize yields at household and village levels, using the multilevel model based on a large panel survey dataset in the NCP. The results show that: (i) Extreme weather events (drought and flood) would significantly reduce the wheat and maize yields. So, the governments should establish and improve the emergency service system of disaster warning and encourage farmers to mitigate the adverse effects of disasters. (ii) Over the past three decades, the NCP has experienced climate change that affects its grain production. Therefore, it is imperative to build the farmers' adaptive capacity to climate change. (iii) Spatial variations in crop yield are significantly influenced by the household characteristics and the heterogeneity of village economic conditions. Therefore, in addition to promoting household production, it is necessary to strengthen and promote China's development of the rural collective economy, especially the construction of rural irrigation and drainage infrastructures.

Revealing the dynamic effects of fossil fuel energy, nuclear energy, renewable energy, and carbon emissions on Pakistan's economic growth.

The primary goal of this study was to examine the relationship between fossil fuel energy, electricity production from nuclear sources, renewable energy, CO2 emissions, and economic growth in Pakistan. Data ranging from 1975 to 2019 were utilized, and the stationarity of this data was verified through the unit root testing. The dynamic connections between variables were investigated by utilizing the linear autoregressive distributed lag technique. Long-run analysis results uncover that fossil fuel energy, renewable energy use, CO2 emissions, and GDP per capita have a productive relationship with economic progress in Pakistan, whereas electric power consumption, electricity produced from nuclear sources, and energy utilization have an adverse effect on economic growth. Furthermore, the consequences revealed that fossil fuel energy, renewable energy consumption, carbon dioxide emissions, and GDP per capita have a significant linkage to Pakistan's economic growth via short run, whereas we revealed that the variables electric power consumption, electricity produced from nuclear sources, and energy usage have an adversative linkage to Pakistan's economic growth. Feasible progressive policies are required from the Pakistani government to pay more attention for tackling the energy and power sectors' issues in terms of fulfilling the country's energy requirements.

The asymmetric effects of crops productivity, agricultural land utilization, and fertilizer consumption on carbon emissions: revisiting the carbonization-agricultural activity nexus in Nepal.

Agriculture plays an integral part in facilitating socioeconomic development in Nepal. However, it is also associated with environmental concerns which need to be controlled for the sake of ensuring environmental and agricultural sustainability in tandem. Against this backdrop, this current study aims to check whether shocks to the levels of agricultural productivity, land utilization for crop production, and fertilizer consumption influence the carbon dioxide emission figures of Nepal over the 1965-2018 period. The long-run associations between these variables are confirmed from the cointegration analysis. Besides, the outcomes from the asymmetric non-linear autoregressive distributed lag regression analysis show that crop productivity does not influence the emission levels in Nepal. However, a decline in the land area used for crop production purposes is evidenced to trigger higher emissions of carbon dioxide both in the short- and long run. On the other hand, higher fertilizer consumption is found to boost the short- and long-run carbon dioxide emission levels in Nepal. Accordingly, considering the objective of reducing agriculture-based emissions, this study recommends the Nepalese government to adopt policies that can enhance the productivity of low energy-intensive crop production, stimulate green agriculture and non-agriculture activities, and minimize the use of chemical fertilizers in arable lands.

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

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

Examining the carbon emissions and climate impacts on main agricultural crops production and land use: updated evidence from Pakistan.

One of the major challenges to the survival of life on earth is the increasingly evolving climate change. The key source of environmental pollution is global warming. With the combustion of fossil fuels, greenhouse gas (GHG), which is generated in the external environment, is increased and air pollutant as well. The present analysis key intention was to examine the CO2 emission and climatic effects on major agricultural crop production and land use in Pakistan. The study used time span annual data varies from 1970 to 2019, and data stationarity was rectify by utilizing the unit root tests. A generalized method of moments with two-stage least squares technique was applied to expose the variables' association with CO2 emission. The study consequences uncover that the wheat, maize, sugarcane, cotton, bajra, gram, sesamum crops, and land use have constructive association with CO2 emission having positive coefficients with probability values (0.3762), (0.0435), (0.2287), (0.2303), (0.2272), (0.0192), (0.4535), and (0.0017) correspondingly, while rainfall, temperature, rice, jowar, and barley uncovered an adversative linkage to CO2 emission in Pakistan. As Pakistan is an emerging country, potential constructive measures must be introduced in directive to reduce CO2 emissions to improve the agricultural productivity.

Carbonization and atmospheric pollution in China: The asymmetric impacts of forests, livestock production, and economic progress on CO2 emissions.

The accumulation of carbon dioxide in the atmosphere has surged over the years as a consequence of diverse humans activities such as deforestation and farming, in particular. The rapidly growing agriculture and farm mechanization have contributed to substantial increases in energy use and carbon dioxide emissions across the globe. It is hypothesized that agriculture significantly contributes to a country's economy to which China is no exception. Hence, the main intention of the current study was to explore the asymmetrical influences of cereal crop production, forestry production, and economic progress on CO2 emissions in China between 1970 and 2017. The non-linear ARDL (Autoregressive Distributed Lag) bounds testing method was used to determine the short- and long-run dynamics linked with positive and negative shocks to the explanatory variables. The findings indicate that positive shocks to cereal crop production deteriorate the atmospheric quality by intensifying carbon dioxide emissions only in the long run, while the impacts of negative shocks in this regard are statistically insignificant. Ironically, shocks to forestry do not exhibit any significant impact on China's carbon dioxide emission levels. Moreover, carbon dioxide emissions demonstrate a strong progressive association with the positive shocks to energy resources utilized within the Chinese economy. Additionally, positive and negative shocks to economic progress are evidenced to boost and reduce the carbon dioxide emission figures in the long run. Lastly, negative shocks to livestock production are witnessed to increase carbon dioxide emissions only in the short run. Hence, for achieving the Chinese carbon-neutrality agenda, it is recommended to prioritize the use of renewable energy resources, particularly for producing cereal crops, in order to curb carbon dioxide emissions in China. Simultaneously, the Chinese economic growth policies should integrate environmentally-friendly schemes to counter the adversative environmental influences related to the economic progress in China.

Estimating the connection of information technology, foreign direct investment, trade, renewable energy and economic progress in Pakistan: evidence from ARDL approach and cointegrating regression analysis.

The present study aims to investigate the effects of information and communication technology, foreign direct investment, trade and renewable energy use with GDP growth in Pakistan using time series data ranging from 1985 to 2017. Stationarity of data was verified by using unit root tests including ADF and P-P, while an autoregressive distributed lag (ARDL) model was used to check the dynamic association amid prescribed variables with long- and short-run analysis. Furthermore, cointegrating regression analysis with FMOLS, DOLS and CCR was applied to validate the variables causality. The outcomes during long-run analysis show that ICTE, trade and renewable energy have constructive linkage to GDP growth, while foreign direct investment has adverse influence to GDP growth in Pakistan. Similarly, the outcomes from cointegrating regression technique exposed that all variables including foreign direct investment, ICTE and trade have positive and constructive association with GDP growth except renewable energy that causes the adverse association to GDP growth in Pakistan. On the basis of outcomes, we will discuss the policy recommendations.

An asymmetrical analysis to explore the dynamic impacts of CO2 emission to renewable energy, expenditures, foreign direct investment, and trade in Pakistan.

Carbon dioxide emission and GHGs are associated with fossil fuels which have adverse effects on the environment. The key intention of this paper was to determine the asymmetric effect of CO2 emission on expenditures, trade, FDI, and renewable energy consumption in Pakistan. An asymmetrical technique (nonlinear autoregressive distributed lag) was employed to validate the constructive and adverse relation among variables. Furthermore, the Granger causality test was also used to verify the unidirectional association amid variables. Study outcomes revealed that the adverse shocks of renewable energy consumption exposed expressively to upsurge CO2 emission in the short-run dynamics. Conversely, constructive shocks of renewable energy consumption display an adversative association with CO2 emission. Furthermore, the decreasing trend in foreign direct investment tends to impede the detrimental effects of CO2 emission. Additionally, the variable expenditures also create the non-eco-friendly impacts and manifest the positive linkage through CO2 emission. Trade possesses statistically insignificant linkage with environmental degradation. The results also disclose that positive as well as negative variations in the foreign direct investment expose to degrade the environmental eminence. Long-run results suggest the direct association between downward trend in renewable energy consumption and CO2 emission signifying that the pollution level decreases, and the upward trend in renewable energy consumption, however, demonstrates insignificantly positive effects. The results also disclose that positive as well as negative variations in the FDI lead to degrade the CO2 emission. Moreover, it is found that the expenditures soar the issue of pollution again in the long run. Finally, the consequence of trade on CO2 emission is adverse, as the outcome suggests. In order to improve the environmental policies for sustainable growth, the study provides direction toward a sustainable environment by reducing carbon dioxide emission.

Do industrialization, energy importations, and economic progress influence carbon emission in Pakistan.

The largest challenge still remains in enhancing the living conditions and economic progress, while growing the environmental footprint is related to energy. The move towards renewable sources means that energy utilization can be increased and environmental impacts reduced. The key purpose of the present analysis was to investigate the CO2 emissions interaction to industrialization, energy imports, carbon intensity, economic progress, and gross capital formation by using time span data ranging from 1971 to 2019. Variable stationarity was confirmed by utilizing the unit root tests, while quantile regression analysis was utilized to check the CO2 emission influence on the independent variables. Outcomes showed that industrialization has constructive influence with CO2 emission having coefficient (0.161636) with probability value (0.0000). Similarly the variable energy imports, carbon intensity, and gross capital formation have positive coefficients (0.206843), (0.895212), and (0.442922) with probability values (0.2171), (0.0004), and (0.0002) correspondingly that exposed the positive interaction with CO2 emission in Pakistan. The variable economic progress exposed an adverse impact to CO2 emission with having coefficient (-0.002841) with probability value (0.8795). In directive to improve the economic progress, the government of Pakistan should take future action to minimize carbon dioxide emission from different sectors that cause the climate change.

How do climatic change, cereal crops and livestock production interact with carbon emissions? Updated evidence from China.

Carbon dioxide emission and climatic variation have a detrimental influence on the atmosphere as well as on agriculture production. The key aim of the present study was to investigate the influence of carbon dioxide emission on livestock, cereal crops production, rainfall and temperature in China by utilizing the vector autoregressive model and Granger causality test for the period 1988-2017. Variables stationarity was verified by using ADF, P-P and KPSS unit root tests. The outcomes through long-run dynamics exposed that agriculture value added and rainfall have a positive influence on carbon dioxide emission, while cereal crops production, livestock production and temperature have an adverse interaction with carbon dioxide emission. Similarly, the results of the short-run analysis also demonstrate that agriculture value added, cereal crops production, livestock production, rainfall and temperature have a significant influence on carbon dioxide emission with their p-values (0.0488), (0.0885), (0.0263), (0.0096) and (0.5141) respectively. Furthermore, the Granger causality test outcomes also exposed a unidirectional linkage amid the variables. In order to improve agricultural productivity, the Chinese government should take potential steps to minimize the carbon dioxide emission from various industries that trigger climate change.

Asymmetric investigation to track the effect of urbanization, energy utilization, fossil fuel energy and CO2 emission on economic efficiency in China: another outlook.

The accelerated urbanization in China was already coupled with a steadily increasing demand for energy usage. The present study major aim was to determine the asymmetric influence of urbanization, energy utilization, fossil fuel energy and CO2 emission on economic progress in China by using an annual time series data varies from 1975 to 2017. Stationarity amid variables was verified by applying the unit root tests, while non-linear autoregressive distributed lag (NARDL) bounds testing model was used to examine the asymmetric impacts on variables with short- and long-run dynamics. Outcomes revealed that via short-run estimates, the negative shocks of energy usage cause significantly to increase the economic efficiency, but positive shocks of energy utilization display the adverse linkage with the economic progress. Similarly, the negative shocks of GDP per capita growth demonstrate a substantial upsurge in the economic progress, and the positive shocks establish the adverse influence towards economic growth. Further, the outcomes of short-run dynamics also exposed the negative shocks of urbanization significantly affected the economic growth, but positive shocks exposed the adversative influence on economic growth. The outcomes display that fossil fuel energy consumption showed a constructive impact to economic progress, and additionally, the variable CO2 emission also uncovered a positive shocks having significant impact on economic progress. Furthermore, the outcomes of long-run analysis express that energy utilization has negative and positive shocks that expose the adverse influence on economic progress of China. GDP per capita growth exposed the constructive influence on the economic growth in both shocks. The negative and positive shocks of urbanization demonstrate a noteworthy influence on economic growth. The variable fossil fuel energy consumption also exposed an optimistic influence on economic progress, and finally the influence of CO2 emission on economic growth is insignificant as the results exposed. The reducing carbon alteration target aims to be reached for China, and in the next several decades, it will encourage the green energy options in order to decrease carbon dioxide emission to avoid environmental pollution by raising its energy intensity.

Another outlook to sector-level energy consumption in Pakistan from dominant energy sources and correlation with economic growth.

The present study seeks to investigate the sector-level energy consumption of oil and natural gas and to explore the linkage between economic growth, households, agriculture, industry, power, fertilizers, and commercial sector in Pakistan for the period of 1980-2016. The energy sector of Pakistan is facing severe crisis from the last few years due to inadequate production and supply. Long-lasting deficits of natural gas and oil, the two supreme types of fuel in Pakistan, had detrimental consequences for the growth as well as for the economic development. An autoregressive distributed lag (ARDL) method and Granger causality test under vector error correction model (VECM) were employed to check the association among the variables. Furthermore, the innovative accounting method was used to investigate the responsiveness of each variable to another within the study framework. Empirical results show long-run association among the variables, as oil consumption in the agriculture and power sector show a positive effect on Pakistan's economic growth. Similarly, energy consumption from natural gas in the households and fertilizers as well as in the industry sector has had a constructive association with economic growth. In contrast, energy consumption from oil in the households and industry sectors has adverse association with economic growth, while natural gas consumption in the commercial sector has negative linkage with economic growth. Possible steps should be taken by the Government of Pakistan to enhance the production of oil and natural gas from other alternatives to meet the requirements of these sectors.

Does carbon dioxide, methane, nitrous oxide, and GHG emissions influence the agriculture? Evidence from China.

Carbon dioxide emission and greenhouse gas emissions are considered core issue in the world that influence agricultural production and also cause climate change. The present study seeks to investigate the linkage of methane emissions, nitrous oxide emissions, carbon dioxide emission, and greenhouse gas emissions with agricultural gross domestic product in China. The long-term association was checked by using an autoregressive distributed lag (ARDL) bounds testing approach, fully modified least squares method, and canonical cointegrating regression analysis. The results from long-run analysis exposed that carbon dioxide emission and greenhouse gas emissions have positive coefficients that demonstrate the long-run linkage with the agricultural gross domestic product having p values of 0.5709 and 0.3751, respectively. Similarly, results also revealed that agricultural methane emissions and agricultural nitrous oxide emissions have a negative association with the agricultural gross domestic product having p values of 0.1737 and 0.0559. China is a huge emitter of CO2 emission and greenhouse gas emissions. Possible conservative policies are required to form the Chinese government to tackle this challenge to decrease CO2 emission in order to increase agricultural production.