Cross-Sectoral Analysis of Water Usage in Thailand Using Input–Output Model

Abstract

Thailand currently ranks third among the most water-intensive countries in the world. The percentage shares of water demand in the country's agriculture, manufacturing, and service sectors, which are major economic sectors, are 75%, 3%, and 5%, respectively. With the continuous growth of the economy, the demand for water is steadily rising, while the expansion of water supply remains constrained by several factors and the water supply is also affected by climate change. This study uses the input-output model to examine the relationship between water usage and the economic system in Thailand in 2010. The constructed input-output model is the integration of the Leontief inverse matrix, the matrix of water usage, and the details of the gross domestic product (GDP). The model indicates the linkage between GDP expansion and water demand in both direct and indirect usage. The computation result obtained from the model indicates that the agricultural sector is the major water user, with its ratio of direct water use being the highest. The manufacturing sector records the highest ratio of indirect water use, which is influenced by its supply chain comprising the agriculture and service sectors. This model and its results may serve as the main foundation for the design of economic and environmental policies oriented toward optimizing water demand and supply. The model can also be extended and enriched with detailed mechanisms of economic behavior to allow further complex analyzes such as water pricing policies.

Decomposition Analysis for the Comparison and the Comprehension of Conventional Input-Output Impacts’ Indicators: An Empirical Paradigm

“…40-54, 59-66, 85-87;Miller and Blair, 2009, pp. 555-558;Bekhet, 2011;Chuenchum et al, 2018;Kelly et al, 2016;Kolokontes et al, 2019). It must be reminded that, these indicators calculate potential-promising effects, due to the fact that they ignore the sectoral magnitudes, in terms of employment or outputs, into a productive circuit (Kolokontes et al, 2018;Kolokontes et al, 2019).…”

“…Letters from the English alphabet are corresponding to each one sector and are used in the next tables. The fundamental equations that constitute the pylons for an I-O model and the equations for the computation of BLs' and the t.I-Ms' and their synthetic components are the follows (Augusztinovics, 1970;Bekhet, 2011Bekhet, , 2012Cardenete et al, 2017;Chuenchum et al, 2018;Cuello et al, 1992;de Mesnard, 2002de Mesnard, , 2004de Mesnard, , 2007ade Mesnard, , 2007bDietzenbacher, 2005;Dietzenbacher and Lahr, 2004;Freytag and Fricke, 2017;Guang and Wen, 2020;Hirschman, 1958;Ivanova, 2014;Jensen et al, 1979, pp. 20-22;Kelly, 2015;Kelly et al, 2016;Kolokontes et al, 2018Kolokontes et al, , 2019Lahiri, 2000;Lenzen, 2003;Leontief, 1936Leontief, , 1951Lopes and Neder, 2017;Meng et al, 2019;Milana, 1985;Miller and Blair, 2009, pp.…”

Input-Output Models and Derived Indicators: A Critical Review

“…10-26). The "direct" and the "total requirements matrices" which are the pylons in this model, they are also referred in the literature as "direct inputs matrix, direct inputs coefficients matrix, technical coefficients matrix, technological matrix, direct purchases matrix, direct linkages matrix" and as "Leontief's inverse, inputs inverse, interdependence coefficients matrix, direct and indirect purchases matrix and output multiplier matrix", respectively (Chuenchum et al, 2018;Ciobanu et al, 2004;Dietzenbacher, 2002;Eiser and Roberts, 2002;Freytag and Fricke, 2017;Jensen et al, 1979, pp. 20-22;Jones, 1976;Kelly, 2015;Kelly et al, 2016;Miller and Blair, 2009, pp.…”

“…The superscripts "L" and "T" denote the provenance of direct requirement matrix from the Leontief's approach, and the total of a magnitude (total transactions' matrix in this case), respectively. The matrix T Z is constituted from four quadrants (Valadkhani, 2003;Chuenchum et al, 2018). The first quadrant ) (Z depicts the endogenous intermediate…”

“…. These sums are called in the literature sectoral "output multipliers" or sectoral "backward linkages indicators (BLs')" and express the purchasers view or else the view of dispersed spillovers to the rest of economy ( Bekhet, 2011;Cai and Leung, 2004;Chuenchum et al, 2018;Dietzenbacher, 2002;Freytag and Fricke, 2017;Hirschman, 1958, pp. 100-107;Miller and Blair, 2009, pp.…”