Driving Force Analysis of Agricultural Economic Growth Related to Water Utilization Effects Based on LMDI Method in Ningxia, Northwest China

Agricultural economy is usually studied by total factor analysis, while it is uncertain what factors affect agricultural production in the perspective of water utilization. The aim of this study was to investigate driving forces of agricultural economy related to water utilization effects in Ningxia during 2007 to 2017. The logarithmic mean Divisia index (LMDI) method was selected to decompose the driving forces of agricultural production value. Results showed that the agricultural production value increased significantly in 2007–2017 in all of Ningxia and in each city. In terms of the whole region, the effect of agriculture water efficiency played a leading and positive role in the increase of the agricultural production value. The effects of water stress, water utilization structure, and water resource endowment all showed a negative driving force, while population exerted a positive effect. For five cities, the effect of agriculture water efficiency and water utilization structure showed no spatial difference; whereas the other effects expressed different driving forces between cities in the northern plain area and southern hilly area due to varied natural conditions and agricultural activities. The results of this research suggested that the first and foremost strategy of agricultural development and water resource management in Ningxia should be to promote water-saving irrigation and optimize agricultural structure.

Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition Analysis

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO2 EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province.

Using the LMDI Method to Analyze the Change in Greenhouse Gas Emissions in Turkish Sectors

In this study, CO2 emissions of the Turkish economy are decomposed for the 1998–2017 period for four sectors; agriculture, forestry and fishery, manufacturing industries and construction, public electricity and heat production, transport, and residential. The analyses are conducted for five fuel types; liquid, solid, gaseous fuels, biomass, and other fuels. In decomposition analysis, Log Mean Divisia Index (LMDI) method is used. The analysis results point out that energy intensity is one of the determining factors behind the change in CO2 emissions, aside from economic activity. The fuel mix component, especially for the manufacturing industries and construction sector, lowers CO2 emissions during the crisis periods when the economic activity declines. Mainly, it is found that changes in total industrial activity and energy intensity are the primary factors determining the changes in CO2 emissions during the study period. Among GDP sectors, manufacturing industries and construction and public electricity and heat production are the two sectors that dominate the change in CO2 emissions. Additionally, the residential and transport sectors’ contributions have gained importance during recent years. Among the manufacturing industries and construction, the non-metallic minerals sector contributes to CO2 emissions, followed by the chemicals sector.

Study the Trends in Energy Consumption Change for the Transport Service Sectors

This research analyzes the energy consumption of transport service sectors in Vietnam and its changing trend in the past twenty-five years using Input-Output (IO) tables and Logarithmic-mean Divisia index (LMDI) method. IO table of 28 economic sectors in 1996, 2000, 2007, 2012 and 2018 is used to determine energy consumption, in which the transport service sector was always the third or second largest energy consumer, accounting for between 9% and 16% of total energy consumption. LMDI method is used to define influencing factors including transport activity, transport structure, transport intensity, and energy intensity. In these four impacts, the change of transport activity contributes the largest effect (occupied 74.3%), followed by the change of energy intensity (occupied 17.7%) of total increased share for energy consumption. Among the transport service sectors, it is found that Freight transport service by road played the mainstream role in the increasing trends of energy consumption in the period of 2007-2018. In order to improve the energy efficiency of the sector, investments in green transport technologies and modernization of trucks to be more efficient and eco-friendlier will be the key contributors.