Growth trends in the developing world : country forecasts and determinants

The authors present real per capita GDP growth forecasts for all developing countries for the period 2005-14. For 55 of these countries, representing major world regions and accounting for close to 80 percent of the developing world's GDP, they forecast the growth effects of the main forces underpinning growth, assuming that these evolve following past trends. The authors find that for the average developing country the largest growth dividend comes from continued improvement in public infrastructure, followed by the growth contributions of rising secondary school enrollment, trade openness, and financial deepening. The joint contribution of these four growth determinants to average, annual per capita GDP growth in the next decade is estimated to be 1 percentage point. Failure to keep improving public infrastructure alone could reduce this growth dividend by 50 percent. The forecasted growth contributions differ by country qualitatively and quantitatively.Achieving Shared Growth,Economic Theory&Research,Governance Indicators,Inequality,Economic Growth

China’s Energy Economy: A Survey of the Literature

This paper reviews literature on China’s energy economics, focusing especially on: i) the relationship between energy consumption and economic growth, ii) China’s changing energy intensity, iii) energy demand and energy -capital and -labor substitution, iv) the emergence of energy markets in China, vi) and policy reforms in the energy industry. After reviewing the literature, the study presents the main findings and suggests some topics for further study.China; Energy; Literature

Sustainable consumption and production in emerging markets

This special issue addresses sustainable consumption and production (SCP) in emerging markets by examining novel methods, practices, and opportunities. The articles present and analyze top-down sustainability efforts as well as bottom-up efforts on firms, supply chain networks, government regulations, and solution methods. This editorial note summarizes the discussions on the firm's operational attributes, sustainable consumption and production practices, and on evaluation and implementation methods. A dominant finding is that the issues of SCP should be explored in different ways within different contexts in emerging countries

ICT and the Environment in Developing Countries: an Overview of Opportunities and Developments

Both developed and developing countries face many environmental challenges, including climate change, improving energy efficiency and waste management, addressing air pollution, water quality and scarcity, and loss of natural habitats and biodiversity. Drawing on the existing literature, this paper presents an overview of how the Internet and the ICT and related research communities can help tackle environmental challenges in developing countries. The review focuses on the role of ICTs in climate change mitigation, mitigating other environmental pressures, and climate change adaptation.information and communication technology (ICT), environment, climate change, mitigation, adaptation.

Forecasting methods in energy planning models

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs

Preparing for future e-waste from photovoltaic modules: a circular economy approach

[EN] The increasing adoption rate of photovoltaic power generation shows that renewable energies have a bright future. Yet, this could be overshadowed by the unintended consequence of increased generation of Waste of Electric and Electronic Equipment (WEEE) at the installations End-of-Life (EoL) stage. As countries find themselves dealing with the increasing WEEE issue, they may adopt different practices which, if wrongly implemented, could potentially backfire, creating additional issues especially among vulnerable social groups. This work proposes improving the WEEE management system by including the Informal Recyclers in the equation, benefitting social groups and material recovery through by delivering materials along different streams in the closed-loop supply chain. The proposed model intends to support the circular economy approach on waste management systems.The authors would like to thank the Instituto Brasileiro de Desenvolvimento e Sustentabilidade (IABS) for their interest and support on our research. Additionally, we would like to thank the projects funding David Hidalgo-Carvajal’s research: the WEDISTRICT project [founded by the European Union’s Horizon 2020 research and innovation programme under grant agreement N°857801], the “Campus UPM Circulares” project within the UPM Research Program [Programa Propio UPM 2020. Acción Estratégica en Ciencia y Tecnología], and “The Circular and Regenerative Campus” community from the EELISA European University Alliance.Hidalgo-Carvajal, D.; Carrasco-Gallego, R. (2022). Preparing for future e-waste from photovoltaic modules: a circular economy approach. International Journal of Production Management and Engineering. 10(2):131-141. https://doi.org/10.4995/ijpme.2022.16712OJS13114110

Oil Consumption Forecasting using ARIMA Models: An Empirical Study for Greece

Oil is considered one of the most widely used commodity worldwide and one of the most important goods for a country's productivity. Even if the effect of renewable energy sources tries to replace the consumption of fossil fuels, such as oil, nonetheless the level of worldwide oil consumption hasn't changed. Forecasting oil consumption plays an important role on the designing of energy strategies for policy makers. This paper aims at modeling and forecasting oil consumption in Greece using Box-Jenkins methodology during 960-2020. Forecasting oil consumption was accomplished both with static and dynamic procedure, in and out-of-sample using various forecasting criteria. The results of our paper present a downturn in oil consumption for the following years due to two basic factors. The first is referred to Covid-19 pandemia where economic activity of the country decreased as well as business revenues. The second is the efforts made by the country to replace, oil consumption with other energy forms such as natural gas and mostly renewable sources like sun and wind. With these actions taken, the country – member of EU is consistent with the regulations signed to Kyoto protocol where there are commitments for CO2 reduction emissions and improvement of energy use. Keywords: Oil Consumption, ARIMA model, Box-Jenkins methodology, forecasting, Greece. JEL Classifications: C52, C53, Q43, Q47 DOI: https://doi.org/10.32479/ijeep.1123

Forecasting High Speed Diesel Demand in India with Econometric and Machine Learning Methods

According to International Energy Agency (IEA), India is expected to surpass China by 2024 to become the second largest consumer of oil in the world followed by the United States. High-Speed Diesel (HSD) has the biggest share in the total petroleum products consumed in India accounting for around 38% of the total consumption. Considering the volatile global oil market and an oil import dependency ratio of more than 80% during the last four years, the probability of supply disruptions is high in the Indian context. As any uncertainty about the supply of diesel can affect the smooth functioning of the economy and may create inflationary pressures. Accurate forecasting of HSD demand will be essential for appropriate supply management arrangements. Artificial Neural Networks (ANN) with Multi-Layer Perceptron (MLP) and extreme learning machines is used for forecasting diesel demand in this study. Demand forecasting has been carried out using monthly HSD demand data drawn from the “Indiastat” database for the period 1991-2022. Comparison of ANN with traditional forecasting methods of Autoregressive Integrated Moving Average(ARIMA)and Exponential Smoothing has also been undertaken in this study. This study identifies the deep learning technique of ANN with MLP as the best diesel demand forecasting technique

Beyond technology and finance: pay-as-you-go sustainable energy access and theories of social change

Two-thirds of people in sub-Saharan Africa lack access to electricity, a precursor of poverty reduction and development. The international community has ambitious commitments in this regard, e.g. the UN's Sustainable Energy for All by 2030. But scholarship has not kept up with policy ambitions. This paper operationalises a sociotechnical transitions perspective to analyse for the first time the potential of new, mobileenabled, pay-as-you-go approaches to financing sustainable energy access, focussing on a case study of pay-as-you-go approaches to financing solar home systems in Kenya. The analysis calls into question the adequacy of the dominant, two-dimensional treatment of sustainable energy access in the literature as a purely financial/technology, economics/ engineering problem (which ignores sociocultural and political considerations) and demonstrates the value of a new research agenda that explicitly attends to theories of social change – even when, as in this paper, the focus is purely on finance. The paper demonstrates that sociocultural considerations cut across the literature's traditional two-dimensional analytic categories (technology and finance) and are material to the likely success of any technological or financial intervention. It also demonstrates that the alignment of new payas- you-go finance approaches with existing sociocultural practices of paying for energy can explain their early success and likely longevity relative to traditional finance approaches