In this paper, a multi-objective programming approach integrated with a Leontief inter-industry model is used to evaluate the impact of energy conservation policy on the cost of reducing CO2 emissions and undertaking industrial adjustment in Taiwan. An inter-temporal CO2 reduction model, consisting of two objective equations and 1340 constraint equations, is constructed to simulate alternative scenarios consisting of Case I (no constraint on CO2 emissions), Case II (per capita CO2 emissions at Taiwan year 2000 levels, i.e. 9.97 t), Case III (Case II emission levels with energy conservation), and Case IV (Case II emission levels with energy conservation plus improved electricity efficiency). The empirical results show that the cost of reducing CO2 emissions in Cases II, III, and IV is US$404, US$376 and US$345 per t, respectively. Some policy implications are also elaborated upon in order to assist decision makers with relevant planning.
The issue of global warming has been a foremost concern of the international community since the enactment of the COP III Kyoto Protocol in December 1997. Although Taiwan is not a member of the Annex I signatory countries, according to the experience of the Montreal Protocol of 1988, the enforcement of such international regulations will affect Taiwan's economic development. In other words, Taiwan has to be prudent in evaluating the potential impact of mitigating CO2 emissions on its economic growth and industrial structure.
The purpose of this study is to simulate the cost of reducing CO2 emissions in Taiwan and to formulate appropriate strategies for the government. In order to achieve this objective, multi-objective programming coupled with an input–output model covering inter-temporal periods is constructed to evaluate the cost of reducing CO2 emissions for the Taiwan economy as a whole. Empirical data is collected and various scenarios for mitigating industrial CO2 emissions are simulated. Based on the simulations, the cost of reducing CO2 emissions is estimated. Finally, some policy suggestions are put forward.
The purpose of this paper is to utilize mutli-objective programming and input–output approaches to evaluate CO2 emission reduction costs in Taiwan, and to assess the impact of CO2 mitigation on industrial adjustment. Through mutli-objective programming, we explore policy alternatives for better resource allocation under the conflicting interests of economic growth and environmental protection. The results of our simulation suggest that in the year 2020, with CO2 emissions constraints in place, the economic growth rate in Taiwan will fall from 4.84 to 3.29%, and the focus of the inter-industrial structure will shift from the industrial sector to the service sector. In other words, the GDP output share of the energy-intensive industries in the industrial sector will be greatly diminished. However, through energy conservation, specific industries, such as the technology and service industries are projected to show a 3.42% increase in their growth rates. Moreover, by improving the efficiency of power generation by 10%, the average annual growth rate can further increase to 3.55%.
As for CO2 reduction costs, in the CO2 emissions control case (Case II), the reduction costs will be US$404 per t of CO2. With the energy conservation of specific industries mentioned above, the reduction costs will be US$376 per t of CO2, which is approximately 7.07% less than in Case I. What is more, with 10% efficiency improvements in power generation, i.e. case IV, CO2 reduction costs will be further reduced to US$345, which is approximately 14.63% less than case I. Hence, the government should take appropriate steps, such as tax exemption, in order to encourage industrialists to install energy-saving equipment and to utilize energy more effectively. Such measures would prove useful in softening the impact of CO2 emission controls on Taiwan industry as a whole.
It should be acknowledged that the empirical results derived in this paper are subject to several restrictions. The model employed in this paper is a linear programming model, and it cannot avoid every potential weakness embedded in linear programming models. For instance, programming models are more suitable to the planned economy system than to the free market system. Also, programming models cannot trace the changing paths of planned systems when policy instrument change or system parameters alter. They can only trace the optimal path under a predetermined system. In addition, the “penny switching problem” also remains unsolved in programming models. That is, if the cost of technique M is slightly cheaper than the cost of technique N (say, one penny), the solution of the programming model will totally substitute technique M for technique N. This may not be the case in reality. Whatever kind of policy or strategy is finally implemented, in reality will probably implicate a trade-off between economic efficiency and its social-political acceptability. Furthermore, the linear programming model does not perfectly describe the non-linear environments found in the real world, including the fixed coefficient of carbon emissions. Finally, the input data adopted in the model can never be perfect, and the model itself is simplified in several respects. For example, the technical coefficient of energy sectors in the input–output table is based on the monetary unit in our model, which may change the derived results when the wholesale price index or the exchange rate in an economy varies significantly.
