Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

Abstract

The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO 2 in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO 2 emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand's construction sector by applying a model incorporating the longand short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO 2 emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study's findings reveal that the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model is a forecasting model with an appropriate time period (t − i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO 2 emissions for the next 20 years (2019 to 2038). From the study, the results show that CO 2 emissions in the construction sector will increase by 37.88% or 61.09 Mt CO 2 Eq. in 2038. Also, the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning.

How to peak carbon emissions of provincial construction industry? Scenario analysis of Jiangsu Province

“…Although the construction industry is one of the largest carbon emitters, there are only a few studies on the carbon emission forecast of the construction industry at the national level. Sutthichaimethee and Kubaha [28] established the Ls-Arimaxi-Ecm model and predicted the carbon emissions of the construction industry in Thailand would increase continuously from 43.31mt in 2019 to 59.72mt in 2038. Giesekam et al [29] established the BIEC model and made a scenario analysis on the carbon emissions of the UK construction industry from 2013 to 2030.…”

Modeling CO2 Emission Forecasting in Energy Consumption of the Industrial Building Sector under Sustainability Policy in Thailand: Enhancing the LISREL-LGM Model

“…Thailand accelerated its development by consistently promoting economic growth alongside social development. Consequently, Thailand has achieved significant success, particularly in continuously increasing exports [10,11]. This is evidenced by increased support for exports to various trading partners with a diverse range of products, expanded access to key export markets, increased production capacity for exports, and consistent efforts to build confidence in trading partners regarding product quality.…”

“…These CO 2 emissions have escalated more rapidly than in other sectors, surpassing the carrying capacity that Thailand can sustain. Analyses of past data over both medium (1-10 years) and long-term (1-20 years) periods [13,17,18] reveal a high growth rate of CO 2 emissions [11]. From these data, it is apparent that government policies regarding environmental management have not been successful.…”