The Relationship between Carbon Dioxide Emissions, Economic Growth and Agricultural Production in Pakistan: An Autoregressive Distributed Lag Analysis

Koondhar

et al. 2021

Environ Sci Pollut Res

This study aims to contribute to the literature and examine the causal relationship between Pakistan’s agricultural products export, industrialization, urbanization, transportation, energy consumption, and carbon emissions. For the last four decades, time-series data were used to employ short-run and long-run nexus between the selected variables by analyzing the autoregressive distributed lag model (ARDL). The Granger causality test was analyzed to estimate the causality directions. The unit root test results indicate that all the selected variables are stationary at the level and first difference. The bound test confirmed that all variables are cointegrated at a 1% significance level. Long-run estimates suggest that an increase in energy consumption will increase the export of agricultural products. An increase in urbanization, transportation, and carbon emission resulted in a decrease in agricultural products export in Pakistan. In the short run, an increase in industrialization, transportation, and energy consumption leads to an increase in agricultural products export. Increasing urbanization and carbon emission decrease the agricultural products export of Pakistan. Based on our findings, we recommend sustainable agricultural production, renewable energy consumption, low carbon emission technologies, and a green portfolio for sustainable agricultural products export.

Section: Resultscontrasting

confidence: 78%

Dynamic linkage between industrialization, energy consumption, carbon emission, and agricultural products export of Pakistan: an ARDL approach

Koondhar

et al. 2021

Environ Sci Pollut Res

“…According to these authors, Rehman et al [118] and Ali et al [119], the government in Pakistan should support the growth of the AVA, because it will contribute to a greater use of renewable energy and, consequently, lower emissions of pollutants into the environment. Additionally, it is necessary to introduce modern technologies [120]. The authors suggest that increasing international economic exchange will allow the agricultural sector to develop and benefit from the transfer of renewable energy technologies.…”

Section: Discussionmentioning

confidence: 99%

Energy Self-Subsistence of Agriculture in EU Countries

et al. 2021

The paper’s main purpose was to identify the level and factors influencing the consumption of bioenergy of agricultural origin in agriculture in EU countries. All EU countries were deliberately selected for research, as of 31 December 2018. The research period covered the years 2004 to 2018. The sources of materials were the subject literature, Eurostat data, and IEA (International Energy Agency) data. The following methods were used for the analysis and presentation of materials: descriptive, tabular, graphical, Gini concentration coefficient, Lorenz concentration curve, descriptive statistics, Kendall’s tau correlation coefficient and Spearman’s rank correlation coefficient. In the EU, there was a high level of concentration of renewable energy consumption in several countries. There was also no change in the use of bioenergy of agricultural origin in agriculture, but the concentration level was low. The degree of concentration has not changed for both parameters of renewable energy over a dozen or so years, which proves a similar pace of development of the use of renewable energy sources in individual EU countries. Higher consumption of bioenergy of agricultural origin in agriculture was shown to occur in economically developed countries, but with high agricultural production. There was a strong correlation between the consumption of bioenergy of agricultural origin in agriculture for the entire EU and individual economic parameters in the field of energy and agriculture. The relations were positive for all economic parameters, for total renewables and biofuels consumption and for agricultural production parameters. Negative relations concerned the total energy consumption and parameters related to the area of agricultural crops.

“…(5) Agricultural economic development level (AEDL). The agricultural economic development level may have a close relationship with agricultural environmental quality according to the theory of the environmental Kuznets curve (Grossman and Krueger, 1995;Ali et al, 2019). This study therefore applies the per capita agricultural output value to represent the agricultural economic development level.…”

Section: Independent Variable Selectionmentioning

confidence: 99%

Evaluation and Influencing Factors of Agricultural Eco-efficiency in Jilin Agricultural Production Zone Based on Super-SBM and Panel Regression Methods

Yan-hua¹,

Tong²,

Lin³

2021

Preprint

Agricultural eco-efficiency is a meaningful index that assess the agricultural sustainable development. Based on the super SBM-DEA approach incorporating agricultural carbon emissions and panel data regression, this study evaluates agricultural eco-efficiency and then investigates the influencing factors in agricultural production zone of Jilin Province. The empirical results show the following: (1) During the observation period, the average agricultural eco-efficiency exhibits a flat “M-shaped” fluctuating trend, a trend of fluctuant growth with phase characteristics, and the agricultural eco-efficiency of each county still has much room for improvement. (2) Significant spatial differences exist in agricultural eco-efficiency across counties. All of the studied counties, except for Nong’an, Huadian, Lishu, Yitong, Gongzhuling, and Qianguo, need to change the input and output structure to optimize agricultural eco-efficiency. (3) The panel data regression model verifies that the agricultural technology extension level, agricultural economic development level, agricultural industry structure, agricultural mechanization intensity and urbanization level have close correlations with agricultural eco-efficiency. (4) The research findings have important implications for policy makers formulating agricultural environmental policies in accordance with the local conditions of various counties.