Existing agricultural ecosystem in China leads to environmental pollution: an econometric approach

Do changes in land use, water bodies, and grazing pastures have a detrimental influence on environmental quality? Opportunities and threats to long-term growth

Land use activities mainly for economic and agricultural purposes have converted one third to one half of our planet's land surface into urban expansion and agricultural practice, which has had significant impacts on natural ecosystems, food production, and environmental quality, attracting the attention of researchers and policymakers. Consequently, land use is emerging as a fundamental issue in global environmental change and sustainable development. This study represents an addition to the prevailing literature by investigating the asymmetric impacts of land-use and land-cover changes on environmental quality in Pakistan using time series data from 1961 to 2016. Carbon dioxide (CO2) emissions were deemed a dependent variable (a proxy for environmental quality), whereas built-up land, cropland, water bodies, and grazing land were considered independent. A nonlinear ARDL bound testing technique (NARDL) was used to investigate dynamic cointegration among the study variables. Moreover, this study used the BDS test and structural break unit root test to confirm nonlinearity and stationarity of the data set. The results confirm that the variables exhibit asymmetrical co-integration. There is a symmetric unidirectional causation, running from built-up land and grazing land towards CO2 emissions with coefficients of 10.570 and 17.045, respectively. Furthermore, asymmetric causality shows that any positive shocks to built-up land (6.134) and water bodies (20.335) significantly cause CO2 emissions. Similarly, a negative shock to grazing land (16.470) also causes CO2 emissions. By contrast, a neutral effect was found between cropland and CO2 emissions.

Towards a clean production by exploring the nexus between agricultural ecosystem and environmental degradation using novel dynamic ARDL simulations approach

Agriculture, which serves as a lifeline for us, is unequivocally vital for an agriculture-dependent economy like Bangladesh, not only for its food supply but also because of its significant contribution towards achieving Sustainable Development Goals (SDGs) 1 and 2. However, in a third-world nation like Bangladesh, where farming practices largely circumvent the environmental consequences, raised our concern. In this milieu, this study is a novel attempt to explore the association between agricultural ecosystem and environmental degradation in Bangladesh using a long time spanning from 1972 to 2018. We observed a long-run association between the agroecosystem and CO₂ emission. Further, findings from the dynamic autoregressive distributed lag (DARDL) simulation model revealed that the environmental quality of Bangladesh is heavily distorted by total cereal production, total livestock head, enteric methane emissions, N₂O emissions from manure application, and CO₂ equivalent N₂O emissions from synthetic fertilizers in the short and long run, whereas agricultural technology, pesticide use in agriculture, and burned biomass crop residue deteriorated the environmental quality only in the long run. The counterfactual diagram entailed from the DARDL model projected the trend of CO₂ emission in response to positive and negative changes in the analyzed variables. Lastly, this study established a causal relationship between the agroecosystem and environmental degradation using frequency domain causality. Indeed, our study will aid in reshaping agricultural practices in an eco-friendly manner to mitigate environmental degradation and help formulate pragmatic policy actions so that agro-lead nations can thrive in the race of achieving SDGs 1, 2, and 13.

Flood hazards and agricultural production risks management practices in flood-prone areas of Punjab, Pakistan

Climate induced disasters, more specifically floods, have caused severe damage to the agriculture sector in Pakistan. These climatic risks have constrained the farming community to adopt risk management strategies to overcome such climate change risks. This research work attempted to examine the association of risk management tools with farmers' perception of risk, risk averse attitude, and various socioeconomic factors. The study has employed the sample data of 398 farmers from two high-risk flood-prone districts of Punjab, Pakistan. The multivariate probit model was used in this study to investigate the association of dependent and independent variables. The findings of the study indicated that small farmers consider heavy rains and floods severe risks to their agricultural production and are more risk averse than large farmers. Estimates of a multivariate probit model interpreted as age of farmer (0.036), heavy rains risk perception (0.597), and landholding size (0.114) were positively related with the risk management tool of depletion of assets. Farmers' education (0.056), off-farm income (3.47), age (0.018), and risk averse attitude of farmer (0.687) were positive, whereas experience of farming (-0.037) was negatively linked with reduction of consumption. Furthermore, experience of farming (0.005), risk averse attitude (0.493), heavy rains (0.481), and flood risk perception (0.536) were positively related with diversification adoption. The flood-prone farming community is more vulnerable to these climatic risks and rely on traditional strategies for risk management. There is a need to adopt crop diversification based on developing research capacity for innovative crop varieties having resistance to floods and climate change affects. Some significant policy measures, such as a more resilient scenario of climate change and floods, need to stimulate activities of enterprise diversification, opportunities of diversifying employment, and strengthening activities of off-farm employment for the sound livelihood of flood-prone farmers and to minimize severe affects of climatic risks.

Environmental quality and the asymmetrical nonlinear consequences of energy consumption, trade openness and economic development: prospects for environmental management and carbon neutrality

Economic expansion gives rise to modern and energy-efficient technologies and, thus, contributes to a decline in energy usage. Developing countries, including Pakistan, require tremendous efforts to sustain economic growth. However, to attain economic growth, these countries have to cope with greenhouse gas (GHG) emissions and other environmental problems. This research focuses primarily on the asymmetric impacts of energy consumption and economic growth on Pakistan's environmental quality. Accordingly, secondary data spanning from 1971 to 2018 was used, and carbon dioxide emission (CO2) was considered a target variable (a proxy for environmental quality), whereas energy consumption (E) and gross domestic product (GDP) as a proxy for economic growth, and trade accessibility (TR) and foreign direct investment (FDI) as control variables. The nonlinear autoregressive distributed lag (NARDL) approach is used to verify the asymmetric co-integration between the variables selected. Moreover, to examine data stationarity and nonlinearity, we used the Zivot-Andrews structural break unit root and BDS tests, respectively. The findings confirmed the asymmetric and symmetric co-integrations among the considered variables. In addition, the causality analysis reveals that only negative shocks to TR have an effect on CO2 emissions. Similarly, negative shocks to FDI asymmetrically cause CO2 emissions. Meanwhile, GDP symmetrically affects CO2 emissions. Finally, a neutral causal response was observed between E and CO2 emissions. These findings have policy implications in terms of environmental management and carbon neutrality, and they serve as a baseline for future research.

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.

Agricultural chemicals and sustainable development: the agricultural environment Kuznets curve based on spatial panel model

Excessive delivery of agricultural chemicals seriously threatens the ecology and environment of agricultural areas and restricts the sustainable development of agriculture. The analysis of agrochemical Environmental Kuznets Curve (EKC) adopting spatial econometric tools is limited. Therefore, this study adopted the spatial panel regression approach to analyze the agricultural chemicals EKC Three Gorges Reservoir Region (TGRR). The results show that (1) both EKC curves of chemical fertilizer and pesticide of the TGRR are inverted U-shaped, and there are 53.8% and 42.3% of the counties/districts did not meet the inflection point of the EKC as regards to chemical fertilizer and pesticide. (2) The EKC of agricultural chemicals of the TGRR are stable, and the variables such as cultivated area and the urban-rural income disparity have impact on the occurrence of the inflection point of EKC. (3) There is the spatial "imitation and convergence" of agricultural chemicals among the counties in the TGRR. The findings indicate that the ecological and environmental situations of agriculture in the TGRR need urgent attention. Countermeasures aiming to alleviate the contradiction between ecological and economic development should be put forward.

Does the prevailing Indian agricultural ecosystem cause carbon dioxide emission? A consent towards risk reduction

Recently, due to the increasing concentration of carbon dioxide emissions in the atmosphere, the global environment has changed dramatically. As a result, climate change, global warming, and environmental degradation-like issues have been raised. Presumably, human beings, as well as the agricultural ecosystem, become most vulnerable to these issues. In this context, the study focuses on the nexus between Indian's agricultural ecosystem and carbon dioxide emission. For the purpose of this study, annual time series data over the time span from 1990 to 2014 was used. The modern techniques, for instance, Johansen, ARDL, and Granger causality, were employed. The Johansen test and ARDL model suggest that carbon dioxide emissions and agricultural ecosystems are co-integrated. In the short run, the ARDL model suggests that a 1% increase in biomass-burned crop residues, total pesticides, and stock of livestock would increase carbon dioxide emissions by 0.26, 0.3, and 6.58% respectively. The residual diagnostics tests suggest that the ARDL model is stable, reliable, and credible in the present form. The results of the granger causality show that a unidirectional causality was found between carbon dioxide and total heads of livestock, all animal manure applied to the soil, agricultural technology, and total pesticide used in the agricultural sector. In contrast, bidirectional causality was found between the production of biomass-burned crop residues and carbon dioxide emission. Therefore, it is suggested that the government should take preemptive action to reduce the risk of environmental pollution and degradation through synchronized strategies particularly by reducing the amount of biomass-burned crop residues and usage of total pesticides in the country.

Hierarchical regression approach to quantify farm households' pro-environmental behavior

The conflict which exists between a households' self-interest and pro-environmental behavior has led to emotional barriers in ecological conservation. We propose that self-construal affects preferences in this conflicting choice, and self-control plays the mediating role. This study provides conflicting decisions associated with the households' green pesticide adoption behavior in China. Individuals were inquired to select between environmental (pro-environmental) and cost-effective (self-interested) pesticide for food security. Hierarchical regression analysis was employed to progress standard regression estimates by adding a second-stage prior regression to an ordinary model and provides a practical method to evaluate multiple exposures. The results show that the hierarchical regression model may improve environmental protection behavioral studies by including socioeconomic, demographic, and psychological factors. Moreover, our study aims to examine the mediating effect of self-control on the relationship between "self-construal" (SC) and "conflict of self-interest and pro-environmental behavior" (CIPB). Results indicate that self-control (coefficient, - 0.0739; significant level, 5%) partially mediates the relationship between SC and CIPB. Our findings suggest that compared with independent self-construal (coefficient, - 0.05; significant level, 5%), the respondents with interdependent self-construal (coefficient, - 0.09; significant level, 1%) favor pro-environmental choices as they are better in applying self-control and perform pro-environmental behavior. The finding reveals that the hierarchical regression approach provides significant advantages in studying a rural households' pro-environmental decision-making. The current research has policy implications for the adoption of environment-friendly pesticide and organic fertilizer.

The inhabitants' dual interest preferences and their impact on pro-environmental behavior in China

The land use changes and farmers' unreasonable land use behaviors continue to threaten China's agricultural land, exacerbating the impact of pollution. The factors that persuade farm households to perform pro-environmental actions are preliminary efforts to strengthen environmental protection. The current study aims to better understand how the dual interest preferences of rural households are interrelated and influence their environmental behavior. A structured questionnaire was employed to collect the primary data from 4 provinces in China to develop new methods to measure the dual interest preferences of farmers and to study their impact on pro-environmental behaviors. The structural equation model (SEM) in Stata14 was used to analyze the relationship between latent and observed variables and to understand their impact on farmers' environmental behavior. The results showed that all the observed variables have the expected signs and have a significant relationship with their latent variables. With the coefficients of 0.76, 0.88, and 0.64, the underlying variables related to the households' dual interest preferences are statistically significantly correlated. The coefficient 0.34 of the latent variable ensures a direct and significant impact on farm households' pro-environmental behavior, suggesting that non-uniformity preferences or conflicts exist between the short- and long-term economic interests. Similarly, a positive and significant coefficient of 0.28 suggests the non-uniformity of preferences in short-term economic and social interests. All the fitness indices ensured that our model fits well. To improve the environment and land quality, the current research has policy implications for the adoption of environment-friendly pesticide and organic fertilizers.

Impact assessment of land use change on surface temperature and agricultural productivity in Peshawar-Pakistan

The profound appreciation of urban expansion and land use change (LUC) considerably influences the ecosystem functions, services, and biodiversity along with the local and regional climate. Land use has undergone an awful transformation due to rapid urbanization and population growth, which in turn increased land surface temperature (LST) in district Peshawar, Pakistan. The current study tends to capture the influence of land use on LST and agricultural productivity by employing multi-temporal, multispectral satellite data and agricultural production data during the selected years, i.e., 1996, 2003, and 2016. The results demonstrated that barren land considerably decreased while the urban area increased over time in all three phases. Furthermore, significant LST difference was found in different land cover units; e.g., barren land and urban area have the maximum, while water bodies followed by vegetation retain minimum LST in all three phases, i.e., 1996, 2003, and 2016. Similarly, the results from agricultural production revealed that except for wheat crops, which decreased by 7.54% during 1999-2003, the production of all major food crops increased during the selected years. However, the production of sugar cane and barley experienced considerable reduction during the selected years, except for barley, which increased by 22.86% during 2003-2016. The finding of this study provides guidance, policy recommendations, and reference for future researchers. Graphical abstract .