Title : Eco-Efficiency Analysis of Processing Industries in Indonesia: Case Study in Energy Intensive Industries
Author : Deni Kusumawardani
Universitas Airlangga
Item Type : Thesis (Dissertation)
Abstract
The main objective of this research is to analyze the eco-efficiency of processing industries in Indonesia with case studies in energy-intensive industries. Specifically, these objectives are divided into four studies: (1) measuring and comparing eco-efficiency; (2) measure and compare technology gaps; (3) decomposing sources of eco-inefficiency; and (4) identify the determinants of eco-inefficiency. In this research, energy-intensive industries include eight types of industry, namely palm oil processing, textiles, pulp & paper, chemicals, fertilizer, glass & ceramics, cement, and metal & steel. The analysis was carried out both in aggregate for energy-intensive industries and by industry type using annual firm-level micro data from 2010 to 2015. Eco-efficiency was measured using a meta-frontier analysis framework which is able to accommodate heterogeneity in production technology between industry types. Data Envelopment Analysis (DEA) is applied as a frontier estimation model and radial Directional Distance Function (DDF) as an eco-efficiency measurement method. Meta-frontier produces two types of eco-efficiency measurements, namely eco-efficiency against meta-frontier (MEE) and group-frontier (GEE). The technology gap is measured by the meta-technology ratio (MTR) which is defined as the ratio of MEE to GEE. Differences in eco-efficiency (MEE) and technology gaps between types of industry were tested statistically using the Kruskal-Wallis Test. Furthermore, in the meta-frontier framework, eco-inefficiency (MTI) can be decomposed into two parts, namely eco-inefficiency caused by technological gaps (TGI) and by managerial failure (GMI). Meanwhile, the determinants of eco-efficiency are identified using the Tobit Panel Model. This research produces several conclusions. First, the average eco-efficiency score of energy-intensive industries on the meta-frontier (MEE) in the 2010-2015 period was 0.687. According to industry type, the highest average MEE score was achieved by the cement industry (0.802), while the lowest was achieved by the fertilizer industry (0.681). In the same period, the average eco-efficiency score for energy-intensive industries against the group-frontier (GEE) was 0.687. The highest average GEE score was achieved by the cement industry (0.962) and the lowest by the textile industry (0.709). The results of the Kruskal-Wallis test show that there are significant differences in eco-efficiency scores between types of industry in the energy-intensive industry group. Second, the average technology gap (MTR) for energy-intensive industries in the 2010-2015 period was 0.940. According to industry type, the highest average MTR was achieved by the textile industry (0.974) and the lowest by the pulp & paper industry (0.738). The results of the Kruskal-Wallis test show that there are significant differences in the technology gap between types of industry. Third, 82.7% of the eco-inefficiency of energy-intensive industries in the 2010-2015 period came from managerial failure in the form of production shortages and excess CO2 emissions, while the remaining 17.3% came from technological gaps. Meanwhile, sources of eco-inefficiency vary by industry type. In the palm oil, textile, chemical, glass & ceramic processing industries, managerial failure is more dominated by contribution of 68.3%, 92.4%, 64.4% and 84.2% respectively. On the other hand, in the pulp & paper, fertilizer, cement, and metal & steel industries, the technology gap is more dominant with contributions of 85.0%, 62.3%, 80.5%, and 73.8%, respectively. Fourth, the Tobit Panel Model regression results show that the eco-efficiency of energy-intensive industries is influenced by foreign investment, scale effects, composition effects, energy intensity, and carbon intensity. According to industry type, the factors that influence eco-efficiency are as follows: (1) palm oil processing (foreign investment, scale effects, and composition effects); (2) textiles (scale effect, composition effect, energy intensity, and carbon intensity), (3) pulp & paper (energy intensity), (4) chemistry (scale effect, composition effect, and energy intensity); (5) fertilizer (effect of composition, and energy intensity); (6) glass & ceramics (foreign investment, scale effect, composition effect, energy intensity, and carbon intensity); (7) cement (none); and (8) metal & steel (effect of composition and energy intensity). The results of estimation and statistical testing show that the Environmental Kuznets Curve (EKC) hypothesis is not accepted in both energy-intensive industries and all types of industry. Based on these conclusions, this research recommends the long-term development of industrial areas located outside Java to reduce the technology gap, especially in the pulp & paper, fertilizer, cement, and metal & steel industries. In addition, investment in production technology that produces energy efficiency, reduced dependence on fossil energy, and the development of alternative energy is needed to increase eco-efficiency. For further research, it is recommended to use balanced panel data, use other environmental indicators, and apply different approaches, models and measurement techniques.
Keywords: Manufacturing, Industries, Technology
