九州大学 · 経済学
福井秀道教授の研究室は、産業・金融・環境分野における生産性と効率性の分析を柱とし、特に多環境汚染要因を組み込んだ生産性評価モデルの構築と応用を進めています。中国・EU・日本・米国の産業・金融・森林部門を対象に、重み付きルーザー方向距離関数や構造的分解分析を用いた国際比較研究が特徴です。環境パフォーマンスと経済的効率性の両面から持続可能な資源管理や政策効果を解明する研究が展開されています。
Figures are computed from collected data and may differ slightly.
Abstract The objective of this study is to calculate and decompose productivity incorporating multi‐environmental pollutants in C hinese industrial sectors from 1992 to 2008. We apply a weighted Russell directional distance model to calculate productivity from both the economic and environmental performance. The main findings are: (1) C hinese industrial sectors increased productivity, with the main contributing factors being labor saving prior to 2000; (2) The main contributing factors for prod
The objective of this study is three-fold. First we estimate and analyse bank efficiency and productivity changes in the EU28 countries with the application of a novel approach, a weighted Russell directional distance model. Second, we take a disaggregated approach and analyse the contribution of the individual bank inputs on bank efficiency and productivity growth. Third, we test for convergence in EU28 bank productivity as well as in the inefficiency of individual bank inputs. We find that ban
This study analyzes industrial wastewater management efficiency using a Chinese provincial dataset from 2004 to 2014. The weighted Russell directional distance model is used to evaluate the efficiency of management practices. Determinants analysis was conducted based on governmental policy, pollution abatement, and market factors to identify the main drivers of industrial wastewater management efficiency in China. The results indicate that the wastewater management efficiency improved in the eas
Forest ecosystem services are fundamental for human life. To protect and increase forest ecosystem services, the driving factors underlying changes in forest ecosystem service values must be determined to properly implement forest resource management planning. This study examines the driving factors that affect changes in forest ecosystem service values by focusing on regional forest characteristics using a dataset of 47 prefectures in Japan for 2000, 2007, and 2012. We applied two approaches: a
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