隱含碳排放強度驅動因素之跨國及跨期比較分析

為達到2050淨零轉型的目標，歐盟於2021年提出55包裹套案 (Fit for 55)，而我國也於2022年3月正式公布「臺灣2050淨零排放路徑及策略總說明」，以逐步限制二氧化碳的排放量。由於碳排放強度及其驅動因素的變化反映了一國伴隨經濟成長的碳排放潛勢，掌握其歷史變化狀況將有助於一國擬定有效的減排策略。本研究運用多國投入產出模型，搭配OECD所編製之多國投入產出表及各國二氧化碳排放數據，計算各國1995~2018年的總隱含二氧化碳排放強度 (aggregate embodied CO2 emission intensity, AECI)，並運用乘法結構分解分析 (multiplicative structural decomposition analysis, MSDA) 將其分解出三種驅動因素，以探討各國在該期間導致碳排放強度變化的關鍵因素為何？本研究發現，(1) AECI在國家間有顯著的不同，經濟發展程度較高國家的AECI數值普遍低於開發中或新興國家；(2) 各國的AECI普遍呈逐年下降的趨勢；(3) 乘法SDA的結果顯示，排放強度的變化是影響AECI高低的最重要因素，因此有效降低排放強度之政策將是改善AECI的最有效政策。

關鍵字

OECD ；多國投入產出模型；碳排放強度；乘法結構分解分析

並列摘要

To achieve the goal of net-zero transition in 2050, the European Union proposed the Fit for 55 in 2021. Taiwan also officially announced the "Taiwan’s Pathway to Net-Zero Emissions in 2050" in March 2022 to gradually limit carbon dioxide emissions. Since changes in carbon emission intensity and its driving factors reflect a country's potential for carbon emissions along with economic growth, understanding its historical changes will help a country formulate effective emission reduction strategies. This study employs a multi-country input-output model, combined with the multi-country input-output table and each country's carbon dioxide emission data compiled by the OECD, to calculate each country's aggregate embodied CO2 emission intensity (AECI) from 1995 to 2018. It also employs multiplicative structural decomposition analysis (MSDA) to decompose this into three driving factors and explore the key elements that lead to changes in carbon emission intensity in each country during this period. This study found that (1) AECI significantly varies among countries, with higher economic development generally corresponding to lower AECI values than those in developing or emerging countries; (2) AECI in all countries generally shows a downward trend; (3) The result of MSDA shows that the change in emission intensity is the most critical factor affecting the level of AECI, so implementing a policy that effectively reduces emission intensity will be the most effective way to improve AECI.