Has energy conservation been an effective policy for Thailand? An input–output structural decomposition analysis from 1995 to 2010

Supasa, Tharinya; Hsiau, Shu San; Lin, Shih Mo; Wongsapai, Wongkot; Wu, Jiunn Chi

doi:10.1016/j.enpol.2016.08.028

Cited by 33 publications

(19 citation statements)

References 24 publications

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“…With regard to the driving factors of CI, research is increasingly being conducted in different countries and regions. Various methods have been used to examine these factors: the LMDI method [22][23][24], the IDA method [25][26], the IPAT model [27], and the STIRPAT model [28] being the most popular. The decomposition method is an important tool employed in exploring the determinants of carbon intensity.…”

Section: Introductionmentioning

confidence: 99%

Regional Distribution of Carbon Intensity and its Driving Factors in China: An Empirical Study Based on Provincial Data

Wu¹,

Dong²,

Zhou³

et al. 2018

Pol. J. Environ. Stud.

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The regional distribution and driving factors of total carbon emissions have been the focus of considerable research. However, carbon intensity rather than total carbon emissions has been selected as the emissions reduction index in China. The Chinese government has committed to reducing carbon intensity by 60-65% from 2005 levels. Currently, limited academic attention has been given to the regional distribution and driving factors of carbon intensity. To explore the means of achieving the carbon intensity target in China, Gini coefficients were employed in this paper to investigate regional differences in carbon intensity across 30 provinces from 1995 to 2014. Moreover, the FGLS (feasible generalized least squares) method was applied to identify the key influencing factors of carbon intensity at the national and three regional levels. The results indicate that: 1. Chinese inter-provincial Gini coefficients of carbon intensity have increased steadily in recent years, which indicates that the difference in carbon intensity between provinces in China has widened. 2. Economic growth, foreign direct investment, and trade openness were negatively correlated with carbon intensity. Conversely, coal consumption, industrial proportion, and urbanization were positively correlated with carbon intensity. Moreover, urbanization has proven to be the most important factor affecting China's carbon intensity. 3. The dominant cause of carbon intensity varies by region. In particular, the dominant cause of carbon intensity in low-and medium-level regions is urbanization. However, the dominant cause of carbon intensity in high-level regions is coal consumption.

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Section: Introductionmentioning

confidence: 99%

Regional Distribution of Carbon Intensity and its Driving Factors in China: An Empirical Study Based on Provincial Data

Wu¹,

Dong²,

Zhou³

et al. 2018

Pol. J. Environ. Stud.

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“…References Period Region Indicator [26] 1973-1978 Sweden Energy use [27] 1966-1980 Denmark Energy consumption [28] 1974-1979 Saskatchewan, Canada Energy use [29] 1963-1977 U.S. Energy use [30] 1971-1984 Taiwan Energy demand [34] 1972-1982 U.S. Energy use [35] 1976-1986 Taiwan Industrial electricity [36] 1981-1987 China Energy use [37] 1987-1992 China Energy intensity [38] 1973-1992 India Energy consumption [39] 1966-1992 Denmark Energy demand [40] 1985-1990 Japan Energy consumption [41] 1990 OECD Energy consumption [42] 1985-1990 Japan Energy demand [43] 1995 EU Energy intensity [44] 1996-2000 Vietnam Energy flow [45] 1970-1995 Japan Energy and emission [46] 1980-2000 Korea Household energy [47] 1992-2004 China Energy intensity [48] 1970-1996 Brazil Energy use [49] 1997-2002 U.S. Energy use [50] 1978-2004 China Embodied energy [51] 1992-2005 China Embodied energy [52] 1970-1985 Japan Energy demand [32] 1997-2007 China Energy and emission [31] 2002-2007 China Energy and emission [53] 1987-2007 China Energy consumption [54] 1987-2005 China Energy intensity [55] 1997-2007 China Energy intensity [56] 1987-2007 China Energy consumption [57] 1997-2007 China and U.S. Energy use [58] 2002-2007 China Energy intensity [59] 1995-2007 Austria Raw material use [60] 1990-2010 Global Energy footprint [61] 1995-2010 Thailand Energy intensity [62] 2001-2011 Catalonian, Spain Energy output [6...…”

Section: Yearmentioning

confidence: 99%

Structural Decomposition Analysis of Driving Factors for Energy Use Before and After the Global Financial Crisis: Evidence from Top Energy Consumer Guangdong Province in China

Wang

Wen

et al. 2019

Pol. J. Environ. Stud.

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Understanding drivers for energy consumption is important for economic and environmentally sustainable development. To explore this issue, the SDA (structural decomposition analysis) method based on input-output theory was used to analyze the influencing mechanism of energy consumption in one of the top energy consumers, Guangdong Province in China, during 2002 to 2012. We divided the process into 2 stages: before and after the global financial crisis. The main conclusions are as follows: 1) Economic activity and population size are the main driving factors for the increase in energy consumption, while energy consumption intensity is the main factor restraining the increment, and the effects of final demand structure on energy consumption transformed from positive before the global financial crisis to negative after the global financial crisis. 2) Analysis of allocation of energy consumption changes caused by final demands shows that international and domestic trade had significant effects on changes in energy consumption. Although energy consumption embodied in international exports decreased after the global financial crisis, it is still the most significant important driver for the increments. Guangdong is a net exporter of embodied

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“…Nonetheless, the reliance on governmental subsidization has also been criticized as a cause of distortion of the actual price and a possible long term negative effect to the market [39]. By controlling retail price for consumers, the lower price of gasoline is related to entail the energy consumption to become higher [39,40].…”

mentioning

confidence: 99%

“…The oil funds are the instrument to stabilize the retail price of bioethanol blended gasoline, which also encourages the consumption of biofuel by maintaining the discount for users of bioethanol blended gasoline type, E20 and E85 (20% and 85% ethanol blending rates, respectively) [38]. Nonetheless, the reliance on governmental subsidization has also been criticized as a cause of distortion of the actual price and a possible long term negative effect to the market [39]. By controlling retail price for consumers, the lower price of gasoline is related to entail the energy consumption to become higher [39,40].…”

mentioning

confidence: 99%

Criteria prioritization for sustainable development of second-generation bioethanol in Thailand with an application of Delphi-AHP technique

Jusakulvijit

Bezama

Thrän

2020

Preprint

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Background: The availability of underexploited agricultural residues in Thailand opens up the opportunity to supply second-generation bioethanol production. The national implementation of residues-to-biofuel can potentially initiate the bioeconomy and greenhouse gas mitigation, requiring engagement from the multi-stakeholders to develop effective policy recommendations. Thus, the study aims to optimize the implementation of the national strategy from a multi-criteria approach to evaluate the concerned aspects and important indicators for second-generation bioethanol development through a participatory prioritization by the actual stakeholders Methods: To analyze the relevance of the different dimensions regarding important levels of criteria, a Delphi-AHP technique was applied. The evaluation process was conducted with different stakeholders and elaborated by using a pairwise comparison of 4 dimensions (main-criteria) and 12 indicators (sub-criteria). The evaluation was applied to the participants to share their preferences evaluating the most important factors from the aspect of technical feasibility, environmental impacts, economic feasibility and social impacts. Results: Bioethanol stakeholders in Thailand from 5 different branches (industry/ business sectors, NPO/NGO, governmental sectors, academic/research institute and financial institution/bank) participated in the Delphi survey. The 20 expert’s evaluation of four dimensions reveals the highest score from the economic feasibility (32.3%), followed by environmental impacts (25.6%), technical feasibility (24.0%) and social impacts (18.1%). From the sub-criteria assessment, the participants selected ‘final price per liter’, ‘added value of input materials’ and ‘net energy balance’ for the top three important indicators among 12 sub-criteria. From the identification of connection between preferable criteria and participants’ expertise, the results encouraged the process of employing different associated backgrounds into account at the step of policy planning in addition to the consideration of affiliations. Conclusions: The stakeholder’s participatory evaluation indicated the importance of economic aspects, highlighting the necessity of the governmental driven policy that needs to be considered. However, implementation scenarios have to be embedded in a broader range of aspects because all the dimension were rated with high impacts. For the future sustainable bioenergy, involvement from stakeholders’ opinions can imply multifaceted scenarios that may interconnect to the social acceptance and benefits for all relevant players when considering the development of advanced bioenergy as a policy recommendation.

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Has energy conservation been an effective policy for Thailand? An input–output structural decomposition analysis from 1995 to 2010

Cited by 33 publications

References 24 publications

Regional Distribution of Carbon Intensity and its Driving Factors in China: An Empirical Study Based on Provincial Data

Regional Distribution of Carbon Intensity and its Driving Factors in China: An Empirical Study Based on Provincial Data

Structural Decomposition Analysis of Driving Factors for Energy Use Before and After the Global Financial Crisis: Evidence from Top Energy Consumer Guangdong Province in China

Criteria prioritization for sustainable development of second-generation bioethanol in Thailand with an application of Delphi-AHP technique

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