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This study aims to examine the effects of energy consumption on economic growth by means of a panel data analysis of 75 net energy-importing countries for the period 1990 to 2012.

For the purpose of the analysis, the countries are classified into two groups, and each group is then classified into subgroups. The first group is formed based on the energy import dependence of the countries and is classified into two subgroups according to whether their dependence is greater than or less than 50 per cent. The second group is formed based on the income level of the countries and is classified into four subgroups, specifically, low-income economies, lower-middle-income economies, upper-middle-income economies and high-income economies.

The findings obtained for both panel data and for each country indicate that there is a positive and statistically significant relationship between energy consumption and economic growth over the long term such that energy consumption contributes more to economic growth as the import dependence of the country decreases. Moreover, the effect of energy consumption on economic growth decreases as the income level of the country increases. This indicates that the efficient use of energy is as important as energy consumption, which is regarded as an important indicator of economic development.

The authors expect that these findings will make a valuable contribution to the results of future studies, as they analyze the relationships among the variables by including the energy intensities of the countries.

Este estudio examina los efectos del consumo de energía en el crecimiento económico, mediante un análisis de datos de panel de 75 países importadores netos de energía para el período 1990-2012.

A los efectos del análisis, los países se clasifican en dos grupos y cada grupo luego se clasifica en subgrupos. El primer grupo se forma en base a la dependencia de los países en materia de importación de energía y se clasifica en dos subgrupos según su dependencia sea superior o inferior al 50%. El segundo grupo se forma sobre la base del nivel de ingresos de los países y se clasifica en cuatro subgrupos: economías de ingresos bajos, economías de ingresos medios-bajos, economías de ingresos medios-altos y economías de ingresos altos.

Los hallazgos obtenidos, tanto para los datos de panel como para cada país, indican que existe una relación positiva y estadísticamente significativa entre el consumo de energía y el crecimiento económico a largo plazo, de modo que el consumo de energía contribuye más al crecimiento económico a medida que disminuye la dependencia de las importaciones del país. Además, el efecto del consumo de energía en el crecimiento económico disminuye a medida que aumenta el nivel de ingresos del país. Esto indica que el uso eficiente de la energía es tan importante como el consumo de la misma, que se considera un indicador importante del desarrollo económico.

Los autores esperan que estos hallazgos aporten una valiosa contribución para estudios futuros, ya que analizan las relaciones entre las variables mediante la inclusión de las intensidades de los países.

Consumo de energía, Crecimiento económico, Importadores netos de energía, Panel de datos

Artículo de investigación

In this study, the author intend to investigate the impacts of renewable energy use and environmental taxation on sustainable development measured by the adjusted net savings (ANS).

This study employs the quantile regression (QR) for a set of 24 Organization for Cooperation and Economic Development (OECD) countries over the period 1994–2018.

The main empirical findings of estimates show that access to renewable energy and environmental taxation generate positive and significant effects in increasing the ANS for most quantiles. Hence, they are practical tools for achieving sustainable development goals (SDGs).

This study has important implications for governments and policymakers of the OECD countries. Therefore, governments can use subsidies and incentives to promote the adoption of renewable energy sources, energy-efficient technologies and sustainable practices. Similarly, by imposing taxes on pollution and resource use, governments can encourage the adoption of cleaner technologies and practices toward more sustainable behavior.

This paper is based on a novel measure of sustainable development (ANS) and a novel econometric method (QR).

Climate change has aroused widespread concern around the world, which is one of the most complex challenges encountered by human beings. The underlying cause of climate change is the increase of carbon emissions. To reduce carbon emissions, the analysis of the factors affecting this type of emission is of practical significance.

This paper identified five factors affecting carbon emissions using the logarithmic mean Divisia index (LMDI) decomposition model (e.g. per capita carbon emissions, industrial structure, energy intensity, energy structure and per capita GDP). Besides, based on the projection pursuit method, this paper obtained the optimal projection directions of five influencing factors in 30 provinces (except for Tibet). Based on the data from 2000 to 2014, the authors predicted the optimal projection directions in the next six years under the Markov transfer matrix.

The results indicated that per capita GDP was the critical factor for reducing carbon emissions. The industrial structure and population intensified carbon emissions. The energy structure had seldom impacted on carbon emissions. The energy intensity obviously inhibited carbon emissions. The best optimal projection direction of each index in the next six years remained stable. Finally, this paper proposed the policy implications.

This paper provides an insight into the current state and the future changes in carbon emissions.

This paper aims to present insights on the governance of sustainability transitions in higher education in Africa. The authors interrogate the research literatures on the governance of socio-technical transitions in water, electricity, transport and waste management, and identify barriers and enabling factors that enhance transformative practices in universities.

The analytical framework proposed in this paper combines the elements of governance network theory (GNT) and transition topology. The framework of this study is grounded in an actor-centric approach using GNT to understand networks conducive to sustainability transitions. Events and governance networks were mapped on a transition topology to visualise organisational and institutional changes over time. The study engaged students, management, academic and administrative staff in building a community of practice towards sustainability. This research is based on qualitative content analysis grounded in interview data, focus group discussions, workshops, webinars and secondary data analysis.

The findings show that the university has consolidated a sustainability vision and targets, but several factors prevent the community from achieving these targets, including hierarchical decision-making processes, a multitude of disjointed committees and fragmentation in the campus community.

This research adds to an emerging body of literature in the field of sustainability in higher education with two contributions. Firstly, the study presents a novel perspective(s) on the governance of sustainability transitions by combining the literatures on governance and sustainability transitions using a new methodological approach of transition topology to show organisational and institutional changes. Secondly, the study presents new empirical evidence for improving the governance of sustainability transitions in a diverse and highly unequal African university community in the process of (de)colonisation of knowledge and governance.

This study provides the critical masses (thresholds) at which the positive incidence of finance and economic growth will be dampened by the negative effects of income inequality and poverty on energy consumption in Sub-Saharan Africa for policy direction.

The study employed the two steps systems GMM estimator for 41 countries in Africa from 2005–2020.

The study found that for finance to maintain a positive effect on energy consumption per capita, the critical thresholds for the income inequality indicators (Atkinson coefficient, Gini index and the Palma ratio) should not exceed 0.681, 0.582 and 5.991, respectively. Similarly, for economic growth (GDP per capita growth) to maintain a positive effect on energy consumption per capita, the critical thresholds for the income inequality indicators (Atkinson coefficient, Gini index and the Palma ratio) should not exceed 0.669, 0.568 and 6.110, respectively. On the poverty level in Sub-Saharan Africa, the study reports that the poverty headcount ratios (hc$144ppp2011, hc$186ppp2011 and hc$250ppp2005) should not exceed 7.342, 28.278 and 129.332, respectively for financial development to maintain a positive effect on energy consumption per capita. The study also confirms the positive nexus between access to finance (financial development) and energy consumption per capita, with the attending adverse effect on CO₂ emissions inescapable. The findings of this study make it evidently clear, for policy recommendation that finance is at the micro-foundation of economic growth, income inequality and poverty alleviation. However, a maximum threshold of income inequality and poverty headcount ratios as indicated in this study must be maintained to attain the full positive ramifications of financial development and economic growth on energy consumption in Sub-Saharan Africa.

The originality of this study is found in the computation of the threshold and net effects of poverty and income inequality in economic growth through the conditional and unconditional effects of finance.

This study investigates the barriers and drivers of using green methods and technologies (GMTs) in supportive educational buildings (SEBs) in South Africa, and assesses their impact on the circular economy (CE) in achieving net-zero carbon goals. While there has been extensive literature on green building technologies, there is limited research on the barriers and drivers of using GMT in SEBs, as well as their impact on the circular economy (CE) in achieving net-zero carbon goals.

This study adopts an interpretivist approach with an ontological basis, using an overarching case study of a SEB at the University of Cape Town (UCT). Semistructured interviews were conducted with executive UCT management, and a field survey of a UCT supportive education building was performed.

At UCT, multiple GMTs have been installed across various buildings to enhance monitoring and management of water and energy consumption. Moreover, initiatives to positively influence student behavior, such as water and energy-saving campaigns around UCT premises, have been introduced. The findings further indicate that UCT has recently emphasized the implementation of GMTs, resulting in improved resource efficiency, CE practices and progress toward achieving net-zero carbon targets for supportive education buildings and the university as a whole.

This research highlights the positive impact of GMTs on a SEB’s CE and net-zero carbon operations. As a result, facility managers should consider incorporating GMTs when planning the development or refurbishment of SEBs.

To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure, energy-saving performance of the whole metro system cannot be guaranteed.

A cooperative train control framework is formulated to regulate a novel train operation mode. The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train. An improved brute force (BF) algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.

Case studies on the actual metro line in Guangzhou, China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters. The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.

Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process, which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation. This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea, where energy-efficient train operation can be realised once train running time is determined, thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

This study aims to report business preferences for achieving net-zero power production emissions in Saskatchewan, Canada as well as business perceptions of the most preferable power production sources, barriers to change and suggestions for improvement. Mixed methods included focus groups and a survey with experimental design. This research demonstrates that this method of advancing academic and business knowledge systems can engender a paradigmatic shift to decarbonization.

The study is a mixed-methods study using five focus groups and a survey which included a 15-min information video providing more information on power production sources (small modular reactors and biomass). Participants requested more information on these topics in the initial three focus groups.

There is a significant gap in Canadian Government targets for net-zero emissions by 2050 and businesses’ plans. Communications, knowledge and capacity gaps identified include lack of regulatory requirements, institutional barriers (including a capacity charge in the event a business chooses to self-generate with a cleaner source) and multi-level governance dissonance. More cooperation between provincial governments and the federal government was identified by participants as a requirement for achieving targets. Providing information to survey respondents increased support for clean and renewable sources, but gender and knowledge are still important characteristics contributing to support for different power production sources. Scientists and teachers were the most trusted sources of information. Power generated from small modular nuclear reactors was identified as the primary future source of power production followed by solar, wind and natural gas. Research results also confirmed the high level of support for hydropower generated in Saskatchewan versus import from Manitoba based on high values of energy solidarity and security within the province.

This study is original, as it concerns upstream system power production portfolios and not failed projects; the mixed-method research design including a focus group and an experimental survey is novel. This research partially addresses a gap in knowledge surrounding which knowledge systems advance paradigmatic shifts and how and whether involving business people in upstream power production decisions can inform decarbonization.

For billing purposes, heavy-haul locomotives in Sweden are equipped with on-board energy meters, which can record several parameters, e.g., used energy, regenerated energy, speed and position. Since there is a strong demand for improving energy efficiency in Sweden, data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.

To monitor energy efficiency, the present study, therefore, develops key performance indicators (KPIs), which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation. Energy meter data of IORE class locomotives, hauling highly uniform 30-tonne axle load trains with 68 wagons, together with additional data sources, are analysed to identify significant parameters for describing driver influence on energy usage.

Results show that driver behaviour varies significantly and has the single largest influence on energy usage. Furthermore, parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions, e.g., axle loads and number of wagons, on energy usage.

Based on the parametric studies, some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived. In the end, some possible measures for improving energy performance in heavy-haul operations are given.