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Ma Jie
School of Management and Economics, University of Electronic Science and Technology of China. No. 2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, Sichuan, PR China

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Journal article
Published: 02 January 2020 in Journal of Cleaner Production
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The disparities in development levels of countries in Africa necessitate correspondingly sensitive categorization approaches because effective renewable energy-related (RER) decisions, policy-making, and subsequent implementation hinges on workable categorization indices. However, studies on sensitive categorization approaches for energy-environment-economy (3E) interdependencies for Africa is rare. This study uses a sensitive characteristic-driven approach to explore the interdependencies in 3E indicators for carbon dioxide (CO2) emissions, renewable energy consumption (REC), fossil fuel energy consumption, gross domestic product (GDP), population, and gross fixed capital formation in 39 African countries. Further, we forecast the dollar values of policy innovations necessary to facilitate the transition to 100% renewable energies for 2030. Using balanced data spanning 2000–2014, the empirical results from the panel vector error-correction model show long-run Granger causality in CO2 emissions, REC and GDP models for Africa, but no evidence of such relationship was found four sub-panels. The results depict varied individual unidirectional and bidirectional causalities among the variables in each panel. Evidence from variance decomposition analysis show that ∼93% of innovations required to achieve renewable energy-led Africa must happen in REC. Results from the high precision own-data-driven forecast puts total energy demand in Africa at ∼25QBtu in 2030 with ∼40% from renewables. Estimates of the dollar value of innovations depict that it would cost Africa ∼ US$3367.2 billion [in 2030 only] to transition to 100% renewables. We find RER investment (RERIs), in billion US$, ranging from 2.69 to 1274.12 for seven sub-panels. Yet, the panel of Moderate Lower Middle-Income Economies need not to make further investments because their existing REIRs could be enough to help the region reach 100% renewables by 2030. The findings herein imply that RER policies in Africa must be intensified to achieve 100% renewable energy target.

ACS Style

Amos Oppong; Ma Jie; Kingsley N. Acheampong; Mark A. Sakyi. Variations in the environment, energy and macroeconomic interdependencies and related renewable energy transition policies based on sensitive categorization of countries in Africa. Journal of Cleaner Production 2020, 255, 119777 .

AMA Style

Amos Oppong, Ma Jie, Kingsley N. Acheampong, Mark A. Sakyi. Variations in the environment, energy and macroeconomic interdependencies and related renewable energy transition policies based on sensitive categorization of countries in Africa. Journal of Cleaner Production. 2020; 255 ():119777.

Chicago/Turabian Style

Amos Oppong; Ma Jie; Kingsley N. Acheampong; Mark A. Sakyi. 2020. "Variations in the environment, energy and macroeconomic interdependencies and related renewable energy transition policies based on sensitive categorization of countries in Africa." Journal of Cleaner Production 255, no. : 119777.

Journal article
Published: 25 February 2018 in Sustainability
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Renewable energy, as an environmentally friendly and sustainable source of energy, is key to realizing the nationally determined contributions of the United States (US) to the December 2015 Paris agreement. Policymakers in the US rely on energy forecasts to draft and implement cost-minimizing, efficient and realistic renewable and sustainable energy policies but the inaccuracies in past projections are considerably high. The inaccuracies and inconsistencies in forecasts are due to the numerous factors considered, massive assumptions and modeling flaws in the underlying model. Here, we propose and apply a machine learning forecasting algorithm devoid of massive independent variables and assumptions to model and forecast renewable energy consumption (REC) in the US. We employ the forecasting technique to make projections on REC from biomass (REC-BMs) and hydroelectric (HE-EC) sources for the 2009–2016 period. We find that, relative to reference case projections in Energy Information Administration’s Annual Energy Outlook 2008, projections based on our proposed technique present an enormous improvement up to ~138.26-fold on REC-BMs and ~24.67-fold on HE-EC; and that applying our technique saves the US ~2692.62PJ petajoules(PJ) on HE-EC and ~9695.09PJ on REC-BMs for the 8-year forecast period. The achieved high-accuracy is also replicable to other regions.

ACS Style

Jie Ma; Amos Oppong; Kingsley Nketia Acheampong; Lucille Aba Abruquah. Forecasting Renewable Energy Consumption under Zero Assumptions. Sustainability 2018, 10, 576 .

AMA Style

Jie Ma, Amos Oppong, Kingsley Nketia Acheampong, Lucille Aba Abruquah. Forecasting Renewable Energy Consumption under Zero Assumptions. Sustainability. 2018; 10 (3):576.

Chicago/Turabian Style

Jie Ma; Amos Oppong; Kingsley Nketia Acheampong; Lucille Aba Abruquah. 2018. "Forecasting Renewable Energy Consumption under Zero Assumptions." Sustainability 10, no. 3: 576.

Book chapter
Published: 21 May 2015 in Advances in Intelligent Systems and Computing
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This paper employed a network approach to investigating the inter-jurisdictional networks formed within the Pan Pearl River Delta (PPRD). We examined the overlapping participation of PPRD members in interlocal agreements of environmental protection, tourism, transportation, S&T and culture, migrant labor, public health, trade. The PPRD members are found to build extensive regional networks to address issues of regional concerns. Although regional economic integration was the initial focus, more collaborative efforts have been devoted to tackle the concerns of minimizing negative externalities brought by rapid social and economic progress. Geographic proximity and resource complementarity play key roles in determining members’ scale and scope of cooperation with each other. The Province of Guangdong occupied the most central positions in all the PPRD networks.

ACS Style

Jie Ma; Liming Suo; Wei Chen. Collaborating in Horizontal Networks of Interprovincial Agreements in Pan Pearl River Delta. Advances in Intelligent Systems and Computing 2015, 543 -551.

AMA Style

Jie Ma, Liming Suo, Wei Chen. Collaborating in Horizontal Networks of Interprovincial Agreements in Pan Pearl River Delta. Advances in Intelligent Systems and Computing. 2015; ():543-551.

Chicago/Turabian Style

Jie Ma; Liming Suo; Wei Chen. 2015. "Collaborating in Horizontal Networks of Interprovincial Agreements in Pan Pearl River Delta." Advances in Intelligent Systems and Computing , no. : 543-551.