Economy-Water Nexus in Agricultural Sector: Decomposing Dynamics in Water Footprint by the Lmdi

Abstract

Traditional economic activities induce environmental pressures. In order to ensure sustainable economic growth, one needs to decouple it from the environmental pressures. Sustainable growth of the agricultural sector is topical in the sense that economic activity supports rural populations, whereas the resulting environmental pressures may affect diverse groups of population. Thus, the analysis of water footprint related to crop farming is important in the sense of efficient resource use and sustainable development of agriculture in general. In this paper, we focus on Lithuanian crop farming and the related green and grey water footprints. Specifically, we decompose the changes in the water footprints during 2000–2016 by exploiting the Logarithmic Mean Divisia Index. Due to the expansion of the areas harvested, the scale effect appeared as an important driver of growth in green and grey water footprints. The shifts in spatial distribution of area harvested virtually had no influence on the dynamics in either of the water footprints. The crop-mix effect was much higher for the grey water footprint (51% over the period of 2000–2015) than it was the case for the green water footprint (21%). The yield growth induced growth in both green and grey water footprints.

Driving factor analysis of spatial and temporal variations in the gray water footprint of crop production via multiple methods: A case for west China

“…In addition, when the indirect influence of the driving factor through other factors was larger, the influence of the two methods was reversed in some regions. Previous studies have shown that the main positive driving factors of the agricultural GWF were economic development status and population Cui et al, 2020), planting structure and scale (Su et al, 2020). The main negative driving factors were investment change in crop planting, urbanization , and industrial structure adjustment (Cui et al, 2020).…”

“…Based on the distribution of the study areas, China is the key area of these studies. The main research methods are structural decomposition analysis (SDA) (Wan et al, 2016;Liao et al, 2021), logarithmic-mean divisia index (LMDI) exponential decomposition (Li et al, 2017;Zhang et al, 2019;Cao et al, 2020;Cui et al, 2020;Su et al, 2020;Feng et al, 2021;Fu et al, 2021;Chen et al, 2022) and stochastic impacts by regression on population, affluence, and technology (STIRPAT) (Jin et al, 2016;Fan and Fang 2020;Zhang et al, 2020) (see Table 1). The SDA method is based on an input-output model and requires input-output coefficients and final demand for each sector (Su and Ang 2012;Jin et al, 2016); however, the LMDI method has no residual term (Ang 2005).…”

Driving factors and decoupling analysis of natural gas consumption in major Organization for Economic Cooperation and Development countries

“…[17][18][19] LMDI model is a factorization method without residual errors, which is widely applied in environment and resource areas, including land, carbon emission, energy, and water resources. [20][21][22][23][24] Therefore, the LMDI model is chosen in this research.…”

China’s Water Intensity Factor Decomposition and Water Usage Decoupling Analysis

“…The LMDI model is a factor decomposition method that does not generate residual error [22]. The application of LMDI is essentially for resources and the environment, such as carbon emissions, energy, land, and water resources [23][24][25][26][27][28]. At present, LMDI research on water resources has calculated the driving factors at national level or the city level [29][30][31][32][33][34][35].…”