An input–output linear programming model for assessing climate policy considering economic growth

Abstract

Deploying new strategies to reduce the effect of climate change may constrain economic growth. It is thus necessary to develop a model which evaluates the trade-off between economic and environmental influences prior to a policy implementation. Recent studies have proved the effectiveness of input–output linear programming model in identifying the optimal solutions when different climate policies are considered. However, analyzing sectoral linkage to give priority sectors and then finding optimal solutions through reducing pollution from these sectors, which help avoid the economic losses from low-polluting sectors, have not been figured out in previous works. This study first uses input–output an (IO) analysis to provide a measure of structural interdependence among economic sectors and present priority sectors. An IO optimization model is then developed for minimizing the total greenhouse gas (GHG) emissions, in order to identify strategies for GHG intensity reduction in Vietnam, focusing on the priority sectors. In addition, the effect of GHG emissions on human health using the disability adjusted life years (DALY) is further evaluated. Six scenarios are considered to identify the potentials of highest GHG intensity reduction that can be obtained by the year 2020. These scenarios encompass BAU, the consideration of different GDP growth rates, differentiated economic sector growth, economic restructure, and the adaptation of lower-pollution technology implementation for the priority sectors. Each scenario quantifies sectoral final demand, sectoral gross domestic output, sectoral GHG emissions, GHG intensity, and DALY. The linkage analysis results indicate that agriculture, fishery and forestry, transport and communication, personal, community and household, manufacturing of non-metallic mineral products, and mining and quarrying are priority sectors. The optimization solutions present that the best strategy is by taking advantages of identified measures. The best solution obtains 20.3% reduction in GHG intensity compared to baseline. These obtained results become the useful suggestions for decision makers and environmental management in designing successful environmental regulations.