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This study aims to investigate the impact of the 2013 privatization of Nigeria’s energy sector on the technical performance of the Benin Electricity Distribution Company (BEDC) and its workforce.

This study used a questionnaire-based approach, and 196 participants were randomly selected. Analytical tools included standard deviation, Spearman rank correlation and regression analysis.

Before privatization, the energy sector, managed by the power holding company of Nigeria, suffered from inefficiencies in fault detection, response and billing. However, privatization improved resource utilization, replaced outdated transformers and increased operational efficiency. However, in spite of these improvements, BEDC faces challenges, including unstable voltage generation and inadequate staff welfare. This study also highlighted a lack of experience among the trained workforce in emerging electricity technologies such as the smart grid.

This study’s focus on BEDC may limit its generalizability to other energy companies. It does not delve into energy sector privatization’s broader economic and policy implications.

The positive outcomes of privatization, such as improved resource utilization and infrastructure investment, emphasize the potential benefits of private ownership and management. However, voltage generation stability and staff welfare challenges call for targeted interventions. Recommendations include investing in voltage generation enhancement, smart grid infrastructure and implementing measures to enhance employee well-being through benefit plans.

Energy sector enhancements hold positive social implications, uplifting living standards and bolstering electricity access for households and businesses.

This study contributes unique insights into privatization’s effects on BEDC, offering perspectives on preprivatization challenges and advancements. Practical recommendations aid BEDC and policymakers in boosting electricity distribution firms’ performance within the privatization context.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Despite South Africa’s ailing electrical grid, substantial renewable energy (RE) integration is planned for the country. As grid-integrated RE affects all grids differently, this study aims to develop an adaptable grid code-guided renewable power plant (RPP) control real-time simulation testbed, tailored to South African grid code requirements to study grid-integrated RE’s behaviour concerning South Africa’s unique conditions.

The testbed is designed using MATLAB’s Simulink and live script environments, to create an adaptable model where grid, RPP and RPP guiding grid codes are tailorable. This model is integrated with OPAL-RT’s RT-LAB and brought to real-time simulation using OPAL-RT’s OP4510 simulator. Voltage, frequency and short-circuit event case studies are performed through which the testbed’s abilities and performance are assessed.

Case study results show the following. The testbed accurately represents grid code voltage and frequency requirements. RPP point of connection (POC) conditions are consistently recognized and tracked, according to which the testbed then operates simulated RPPs, validating its design. Short-circuit event simulations show the simulated wind farm supports POC conditions relative to short-circuit intensity by curtailing active power in favour of reactive power, in line with local grid code requirements.

To the best of the authors’ knowledge, this is the first design of an adaptable grid code-guided RPP control testbed, tailored to South African grid code requirements in line with which RPP behavioural and grid integration studies can be performed.

With the growing climate problem, it has become a consensus to develop low-carbon technologies to reduce emissions. Electric industry is a major carbon-emitting industry, accounting for 35% of global carbon emissions. Universities, as an important patent application sector in China, promote their patent application and transformation to enhance Chinese technological innovation capability. This study aims to analyze low-carbon electricity technology transformation in Chinese universities.

This paper uses IncoPat to collect patent data. The trend of low-carbon electricity technology patent applications in Chinese universities, the status, patent technology distribution, patent transformation status and patent transformation path of valid patent is analyzed.

Low-carbon electricity technology in Chinese universities has been promoted, and the number of patents has shown rapid growth. Invention patents proportion is increasing, and the transformation has become increasingly active. Low-carbon electricity technology in Chinese universities is mainly concentrated in individual cooperative patent classification (CPC) classification numbers, and innovative technologies will be an important development for electric reduction.

This paper innovatively uses valid patents to study the development of low-carbon electricity technology in Chinese universities, and defines low-carbon technology patents by CPC patent classification system. A new attempt focuses on the development status and direction in low-carbon electricity technology in Chinese universities, and highlights the contribution of valid patents to patent value.

The main purpose of the Dubai Electricity and Water Authority (DEWA) sustainability case is to allow students to explore how nonfinancial information reported in sustainability plays a vital role in maintaining a trade-off between current economic pressure and future environmental needs.

This is an exploratory study in nature using a qualitative case study approach. The case requires an examination of DEWA's sustainability reporting (SR) in the context of Global Reporting Initiatives (GRIs). This case is designed to assist students in gauging DEWA's sustainability and explore how the company evaluates the materiality of sustainability issues.

With stakeholders' and investors' increased interest in sustainability, the authors argue that accounting programs should incorporate this topic into their curricula. The case enables students to focus on sustainability-related initiatives with DEWA that are aligned with GRI initiatives. The case might be instructive for both undergraduate and postgraduate students studying environmental and management accounting.

This case study is the first of its kind in the Gulf Cooperation Council (GCC) region to comprehensively analyze DEWA's sustainability practices concerning GRI-based SR. This study widens the understanding of DEWA's implementation of GRI standards in the preparation of its sustainability reports.

Understanding China's carbon dioxide (CO2) emission status is crucial for getting Carbon Neutrality status. The purpose of the paper is to calculate two possible scenarios for CO2 emission distribution and calculated input-output flows of CO2 emissions for every 31 China provinces for 2012, 2015 and 2017 years.

In this study using the input and output (IO) table's data for the selected years, the authors found the volume of CO2 emissions per one Yuan of revenue for the industry in 2012 and the coefficient of emission reduction compared to 2012.

Results show that in the industries with a huge volume of CO2 emissions, such as “Mining and washing of coal”, the authors cannot observe the reduction processes for years. Industries where emissions are being reduced are “Processing of petroleum, coking, nuclear fuel”, “Production and distribution of electric power and heat power”, “Agriculture, Forestry, Animal Husbandry and Fishery”. For the “construction” industry the situation with emissions did not change.

“Transport, storage, and postal services” and “Smelting and processing of metals” industries in China has the second place concerning emissions, but over the past period, emissions have been sufficiently reduced. “Construction” industry produces a lot of emissions, but this industry does not carry products characterized by large emissions from other industries. Authors can observe that Jiangsu produces a lot of CO2 emissions, but they do not take products characterized by significant emissions from other provinces. Shandong produces a lot of emissions and consumes many of products characterized by large emissions from other provinces. However, Shandong showed a reduction in CO2 emissions from 2012 to 2017.

Just transition is a fundamental concept for supply chain management but neither discipline pays attention to the other and little is known about how supply chains can be orchestrated as socioecological systems to manage these transitions. Building from a wide range of just transition examples, this paper explores just transition to understand how to move beyond instrumental supply chain practices to supply chains functioning in harmony with the planet and its people.

Building from a systematic review of 72 papers, the paper identifies just transition examples while interpreting them through the theoretical lens of supply chain management, providing valuable insights to help research and practice understand how to achieve low-carbon economies through supply chain management in environmentally and socially just ways.

The paper defines, elaborates, and extends the just transition construct by developing a transition taxonomy with two key dimensions. The purpose dimension (profit or shared outcomes) and the governance dimension (government-/industry-led versus civil society-involved), generating four transition archetypes. Most transitions projects are framed around the Euro- and US-centric, capitalist standards of development, leading to coloniality as well as economic and cultural depletion of communities. Framing just transition in accordance with context-specific plural values, the paper provides an alternative perspective to the extractive transition concept. This can guide supply chain management to decarbonise economies and societies by considering the rights of nature, communities and individuals.

Introducing just transition into the supply chain management domain, this paper unifies the various conceptualisations of just transition into a holistic understanding, providing a new foundation for supply chain management research.

With more cities aiming to achieve climate neutrality, identifying the funding to support these plans is essential. The purpose of this paper is to exploit the present of a structured green bonds framework in Sweden to investigate the typology of abatement projects Swedish municipalities invested in and understand their effectiveness.

Marginal abatement cost curves of the green bond measures are constructed by using the financial and abatement data provided by municipalities on an annual basis.

The results highlight the economic competitiveness of clean energy production, measured in abatement potential per unit of currency, even when compared to other emerging technologies that have attracted the interest of policymakers. A comparison with previous studies on the cost efficiency of carbon capture storage reveals that clean energy projects, especially wind energy production, can contribute to the reduction of emissions in a more efficient way. The Swedish carbon tax is a good incentive tool for investments in clean energy projects.

The improvement concerning previous applications is twofold: the authors expand the financial considerations to include the whole life-cycle costs, and the authors consider all the greenhouse gases. This research constitutes a prime in using financial and environmental data produced by local governments to assess the effectiveness of their environmental measures.

The Gulf Cooperation Council (GCC) of countries has some of the highest electricity consumptions and carbon dioxide emissions per capita in the world. This poses a direct challenge to the GCC government’s ability to meet their CO₂ reduction targets. In this review paper the current household electricity consumption situation in the GCC is reviewed.

Three scenarios for reducing energy consumption and CO₂ emissions are proposed and evaluated using strengths, weaknesses, opportunities and threats (SWOT) as well as the political, economic, social, technical, legal and environmental (PESTLE) frameworks.

The first scenario found that using solar Photovoltaic (PV) or hybrid solar PV and wind system to power household lighting could save significant amounts of energy, based on lighting making up between 8% to 30% of electricity consumption in GCC households. The second scenario considers replacement of conventional appliances with energy-efficient ones that use around 20% less energy. The third scenario looks at influencing consumer behavior towards sustainable energy consumption.

Pilot trials of these scenarios are recommended for a number of households. Then the results and feedback could be used to launch the schemes GCC-wide.

The proposed scenarios are designed to encourage responsible electricity consumption and production within households (SDG12).

All three proposals are found viable for policymakers to implement. However, to ensure successful implementation GCC Governments are recommended to review all the opportunities and challenges associated with these schemes as laid out in this paper.