Clean energy, non-clean energy, and economic growth in the MIST countries

Pao, Hsiao-Tien; Li, Yi Ying; Fu, Hsin-Chia

doi:10.1016/j.enpol.2013.12.039

Cited by 114 publications

(51 citation statements)

References 30 publications

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“…Adverse effects on energy consumption are found for industrial energy, renewable energy, as well as total final energy consumption (Table 1, Panel 2, column 4, rows 1, 3, and 5). This finding is consistent with the energy-driven growth hypothesis discussed in Aspergis and Tang [24], Pao, et al [25], and Narayan and Doytch [21]. Perhaps, what is more surprising is that we do not see a more prominent effect within the groups of the developing countries (Table 1, panel 1; columns 2 and 3).…”

Section: Resultssupporting

confidence: 80%

“…Pao et al [25], based on earlier work by Apergis and Payne [26] (see references cited in Pao et al [25], Table 1) test the relationship between clean energy (renewables and nuclear energy) and nonclean energy (fossil fuels), and the relationship between renewable energy and economic growth. Among a broader set of empirical results, they find a long-run unidirectional causality between renewable energy demand and economic growth.…”

Section: Energy Security and Economic Developmentmentioning

confidence: 99%

See 1 more Smart Citation

The impact of natural disasters on energy consumption: An analysis of renewable and nonrenewable energy demand in the residential and industrial sectors

Doytch

Klein

2017

Env Prog and Sustain Energy

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This study examines the impact of infrastructure-damaging natural disasters (meteorological and geophysical disasters) on energy consumption differentiating by type of energy: residential versus industrial and nonrenewable versus renewable. We used a novel comprehensive unbalanced data set spanning 50 years for up to 80 countries, which we group by level of development to reduce heterogeneity within the group. We applied an estimation method that takes into account the dynamics of the economic processes in the panel: the Blundell and Bond GMM estimator. For high-income economies, which are also technologically the most advanced, we are able to demonstrate a positive impact on renewable energy use 5 years after the occurrence of a geophysical disaster. For low-income economies, we observe positive effects on industrial energy consumption; for middle-income countries, on residential energy consumption.

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Section: Resultssupporting

confidence: 80%

Section: Energy Security and Economic Developmentmentioning

confidence: 99%

The impact of natural disasters on energy consumption: An analysis of renewable and nonrenewable energy demand in the residential and industrial sectors

Doytch

Klein

2017

Env Prog and Sustain Energy

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“…Following Pao and Li (2014), Shahbaz et al (2014), Sbia et al (2014) and Saboori et al (2012) with modifications, the study adopts the Autoregressive distributed lag (ARDL) approach to cointegration introduced by Pesaran et al (2001) to test the long run equilibrium relationship between economic growth and clean energy indicators. Some of the merit of using the ARDL cointegration approach over other methods include: derivation of the error correction model via a simple linear transformation which combines short run adjustment from shocks with long run without compromising long run information; it can be used irrespective of the fact that variables are stationary at I(0), I(1) or combination of both; ARDL cointegration has a good property for small sample size.…”

Section: Data Model and Methodologymentioning

confidence: 99%

“…They further recommend the amendment of land use act, investment laws and environmental impact assessment decree by Nigerian government. Pao and Li (2014) investigated economic growth, clean energy and unclean energy in MIST (Mexico, Indonesia, South Korea, and Turkey) economies. The method adopted was panel cointegration.…”

Section: Literature Reviewmentioning

confidence: 99%

Does clean energy contribute to economic growth? Evidence from Nigeria

Maji

2015

Energy Reports

107

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a b s t r a c tThis paper examines the impact of clean energy on economic growth in Nigeria. Autoregressive distributed lag (ARDL) approach to cointegration was used to determine the existence of cointegration among the variables. The long run results suggest a significant negative relationship between two indicators of clean energy (alternative and nuclear energy and electric power consumption) and economic growth. The result further reveals a significant positive relationship between combustible renewables and waste and economic growth. Mixture of negative and positive relationships between clean energy indicators and economic growth was obtained in the short, although not significantly different from zero. We concluded that Nigeria has Potentials of clean energy to be reaped in near future.

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“…Mehrara (2007) used GDP per capita and energy consumption per capita as main variables in empirical studies and discovered the one-way (rather than interactive) causality between economic development and energy consumption [10]. Pao et al (2014) started from different economic cycles, and concluded that, in the short term, energy consumption has a one-way causality relation with economic growth, while, in the long term, there is an interactive correlation between the two [11]. In terms of China study on energy consumption and economic development, Lin (2003) used co-integration analysis and error correction model and proved the one-way causality between China's electrical energy consumption and economic growth [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Efficiency, Ownership Structure, and Sustainable Development: Evidence from China

Yang

2017

Sustainability

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Low energy efficiency and severe environmental pollution are two growing issues haunting China's fast economic development. Under China's current development model, economic growth still heavily depends on massive energy input, which makes energy efficiency one crucial topic in order to secure future sustainable development of China. This paper focuses on the unique energy ownership structure of China, and designs and adopts MATLAB programming for optimization solutions of multi-variable constrained nonlinear functions to obtain results that can better reflect China's energy efficiency and solutions to sustainable development of China. Using this model, this paper conducts an empirical analysis on the impact of difference energy investment behaviors between China's state-owned entities and non-state-owned business on China's Total Factor Energy Efficiency (TFEE) and sustainable development from 2003 to 2014. We find that Beijing and Shanghai represent the highest energy efficiency level in China. However, except for the more developed regions in Eastern China, for other provinces in Northeast China, Central China and Western China, the TFEE of energy investment by state-owned and non-state-owned economies are both quite low compared with the optimal level. Based on the above findings from the empirical study and detailed analysis by region, this paper discusses the possible reasons for China's low TFEE and provides implications and policy recommendations.

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Clean energy, non-clean energy, and economic growth in the MIST countries

Cited by 114 publications

References 30 publications

The impact of natural disasters on energy consumption: An analysis of renewable and nonrenewable energy demand in the residential and industrial sectors

The impact of natural disasters on energy consumption: An analysis of renewable and nonrenewable energy demand in the residential and industrial sectors

Does clean energy contribute to economic growth? Evidence from Nigeria

Energy Efficiency, Ownership Structure, and Sustainable Development: Evidence from China

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