Energy Policy Planning Research Articles

Digital data demand and renewable energy limits: Forecasting the impacts on global electricity supply and sustainability

This study critically evaluates whether the current and projected generation of renewable energy can meet the escalating global demand for electricity from digital data growth. Our modelling forecasts reveal a concerning trend: despite the expansion in renewable energy capacities, they are likely insufficient to satisfy the burgeoning electricity needs of the digital data sector. More alarmingly, there is a real risk that the demand for digital data could soon exceed feasible electricity production capabilities. This paper underscores the urgent necessity for a data-centric sustainability approach across all supply chains, sectors, industries, and nations. Such measures are crucial to increase efficiency, cut energy usage, and transition towards a decarbonized digital ecosystem, thereby supporting the global pursuit of a sustainable, net-zero future. This research highlights a critical junction in energy policy and digital infrastructure planning, urging immediate action to reconcile digital advancement with ecological sustainability.

Forecasting Electricity Consumption Trends in India Using ARIMA-Based Regression Analysis: A Focus on Historical Lags

The ability to forecast electricity generation is crucial for effective energy management and policy planning. This study investigates the use of historical electricity generation data as a predictor for future generation trends using an autoregressive integrated moving average (ARIMA)-based regression model. Focusing on lagged values of electricity generation, we assess the predictive accuracy and statistical significance of the lagged variable (LAG1) for forecasting. The results indicate a strong positive relationship between past and future electricity generation, with the LAG1 coefficient being statistically significant at the 1% level. The regression model explains 97% of the variation in electricity generation, demonstrating its high utility for future forecasting. This analysis provides valuable insights for energy policymakers and stakeholders in preparing for future electricity demand.

Oman’s Green Horizon: Steering Towards Sustainability Through Decarbonization and Energy Transition

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2024, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. The analysis utilizes stepwise regression to systematically identify the most significant predictors, ensuring a parsimonious model. Robust least squares (ROLSs) are employed to account for potential outliers and heteroscedasticity in the data, providing more reliable estimates. Fully Modified Least Squares (FMOLSs) is applied to address issues of endogeneity and serial correlation, offering robust long-term coefficient estimates. Canonical cointegrating regression (CCR) further refines these estimates by handling non-stationary variables and ensuring consistency in the presence of cointegration. Cointegration tests, including the Johansen and Engle–Granger methods, confirm long-term equilibrium relationships among the variables; this study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, the Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle–Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman’s efforts towards sustainable development and decarbonization.

Clustering Open Data for Predictive Modeling of Residential Energy Consumption across Variable Scales: A Case Study in Andalusia, Spain

The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock.

Can the energy transition drive economic development? Empirical analysis of China's provincial panel data

With the rise of global environmental awareness and increasing concern about the impacts of climate change, the energy transition has become a pressing issue worldwide. However, in existing studies, the conclusions on the impact of the energy transition on economic development are not consistent. Given these discrepancies and the complexity of the influencing factors in the process of energy transition, the study of the relationship between energy transition and economic development has important theoretical and practical significance for energy policy and economic planning in China and globally. By utilizing provincial panel data from 2000 to 2019 in China and constructing an econometric model, this study empirically examines the influence of the energy transition on economic development. The results show that the energy transition is beneficial for China's economic development. Furthermore, regional heterogeneity significantly stimulates economic development through the upgrading of industrial structure, technological innovation, and environmental regulation. This study provides important empirical evidence and a quantitative basis for actively and steadily promoting the energy transition while considering economic growth.

Utilizing Machine Learning Techniques for Power Estimation in Residential Electricity Consumption

Electricity consumption analysis and estimation play a vital role in various contexts, ranging from business planning to household financial management. In an era where electrical energy is a primary source of human activity, understanding power usage patterns and estimating them becomes crucial. This research enables an understanding of energy consumption from individual to national scales and supports efficient energy policy planning. Power estimation provides insights into the expenditures that consumers or companies will incur, enabling effective financial planning. Research and development of accurate predictive models using machine learning techniques are essential to providing useful information for the energy industry and the general public. This study uses machine learning to compare the performance of different power prediction models. The decision tree (DT) method, with an MAE of 0.3613, an MSE of 0.4184, and an RMSE of 0.6469, gives the best results. The results show that the model can provide accurate predictions of electricity consumption, potentially becoming a reliable tool in power management and financial management. Its contribution is crucial in the energy industry and daily life, providing insights needed to enhance energy efficiency and budget management. As a result, this research provides relevant and beneficial solutions to energy and financial management for society and industry. Keywords: Electricity consumption analysis, Estimation, Machine learning, Power management, financial management

Mitigation of land-related impacts of solar deployment in the European Union through land planning policies

Solar power is space-intensive and will contribute to intensify land competition, a factor typically not captured by models. This study uses the Integrated Assessment Model WILIAM which explicitly represents the land use changes driven by solar energy expansion through a hard link of its energy and land modules including net energy restrictions. A Green Growth type transition is simulated for the European Union with a high renewable energy share target in electricity mix by 2050, testing different land use planning policies.The results show that a rapid deployment of solar power in land without land policies can intensify land use conflicts and increase associated land use change emissions. Land-use requirements for solar would be 1–1.4 % of total land (corresponding to 55–75 % of urban land), which could be problematic locally. The implementation of land-use protection and land siting policies could reduce 23 % of total land occupied bv solar photovoltaics panels (with respect to forest and cropland the area occupied could be reduced up to 88 %), and 23–47 % of the land use change associated emissions with respect to a scenario where not policies are applied. These results show the importance of integrating land use and energy planning policies to alleviate the undesired impacts.

Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

The potential impact of digital economy on energy poverty in the context of Chinese provinces

This study focused on exploring the impact of the digital economy (DE) on energy poverty (EP) across Chinese provinces from 2004 to 2018, motivated by the critical need to understand how technological advancements in the digital sector influence energy accessibility and sustainability. Conducted against the backdrop of global digital transformation, the research aimed to dissect the nuanced ways in which the DE contributes to mitigating EP, employing dynamic panel threshold and indirect effect models to capture both the direct and nuanced, and intermediate effects of digital progress on energy deprivation. Key findings revealed a significant reduction in EP attributed to the advancements in DE, with the most notable improvements observed in Eastern China where strategic energy policies and management practices enhanced the positive impacts of digitalization. The study highlighted the DE's role in improving energy access, efficiency, and environmental sustainability, although it also pointed out the potential for regressive effects in areas with lower levels of technological advancement. These findings are of substantial value as they offer empirical evidence of the DE's capacity to alleviate EP, underlining the importance of integrating digital strategies into energy policy planning. The research provides critical insights for policymakers, stakeholders in the energy sector, and scholars interested in the synergies between digital innovation and energy security, suggesting that leveraging digital technologies could accelerate efforts towards achieving sustainable energy access and combating energy poverty in China and potentially in other contexts facing similar challenges.

Appraising Kenyan energy demand policies for energy efficiency improvement and GHG emissions mitigation

Like in most Sub-Saharan Africa, biomass is the most consumed fuel in overall Kenyan energy demand. In addition to biomass, other end-use fuels are also consumed in the country, mostly by economic sectors. However, electricity demand has been the only end-use energy considered in most energy planning studies conducted for the country. There is a need to develop robust energy planning policies considering all end-use fuels demand in order to give insights on overall supply needed by residential and economic sectors of the country. Therefore, this study used LEAP “Low Emissions Analysis Platform” model to analyze the country's total energy demand in all sectors. Results show that the country's total energy demand would keep rising; this was 685.4 PJ in 2015 and would grow up to 857.5 PJ in 2040. The residential sector would remain the main consumer of total final energy as its consumption would constitute 79.6 % of total energy demand by 2040. Firewood and charcoal would remain the main consumed fuels, especially for cooking while electricity would stay the main consumed energy for lighting. Three energy policies (universal electrification -UE, efficient lighting - EL and efficient cooking stoves -EFCS), found strategic priorities by the Kenyan Government, were analyzed. While the UE will require the country to increase its power supply, the implementation of EL and EFCS policies for highly consumed fuels would enable to save a significant amount of energy, reduce a high rate of deforestation and mitigate GHG emissions. This study would help Kenyan policymakers to strategize on all-fuels energy supply in order to meet the total energy demand.

Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand

Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye’s energy demand. This study utilizes population, GDP per capita, imports, and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation, and their performance was measured using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared, both with each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF, and Ridge as the base learners in the stacking ensemble model yielded the highest R-squared value, which was 0.9882, indicating its superior performance. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of the predictive model but also underscore the broader implications of this study within the framework of the United Nations’ Sustainable Development Goals (SDGs).

Solid Biofuel Production from Biomass: Technologies, Challenges, and Opportunities for Its Commercial Production in Nigeria

Producing durable and efficient solid biofuels should be an important consideration in Nigeria’s present economy due to the numerous advantages associated with it. It offers the benefit of energy generation, particularly in rural areas, and could potentially replace fossil fuels. However, the adoption and production of solid biofuels at commercial scale in Nigeria is limited by some challenges, including the lack of a developed supply chain structure, inadequate facilities, and air pollution. The present study summarizes the types of solid biofuel production technologies deployed in Nigeria as well as the biomass feedstock utilized in the production of fuel briquettes and pellets. While opportunities exist in the gasification of biomass in Nigeria, direct combustion is a readily applicable fuel conversion process that can be utilized to generate electricity from solid biofuel. The major challenges surrounding the full adoption of solid biofuel production and utilization in Nigeria are highlighted. Among others, promotion of clean energy alternatives, investments and financial incentives, sustainable renewable energy policy and energy transition plan, and legislative backing are identified as factors that could accelerate the commercial production and adoption of solid biofuel in Nigeria.

Nuclear energy policy and city planning in the early days of nuclear energy development: Analysis of the construction process of the atomic center in Tokai Village

AbstractTokai Village is a home to the first commercial nuclear power plant in Japan, and Nuclear Power Plants are directly adjacent to residential districts there. This study investigates the context of this situation by looking back around 1960 when the plants were built and clarifying the development project ideas and decision‐making processes that were involved in the planning. The main findings are the following: Japan Atomic Industrial Forum (JAIF) Inc. drafted the development plan for Tokai Village, founded Tokai Atomic Urban Development, acquired company sites. The Ibaraki Prefecture involved JAIF in making decisions on city planning of Tokai Village.

The Goal of Carbon Peaking, Carbon Emissions, and the Economic Effects of China's Energy Planning Policy: Analysis Using a CGE Model.

This study focuses on the effects of China's carbon peaking policy, investigating how to balance nonfossil energy consumption and coal consumption to achieve China's carbon peaking policy goal. The research applies the recursive dynamic computable general equilibrium model to simulate the impact of China's energy planning policies using five scenarios to analyze the carbon emissions and economic effects of China's energy planning policy from the perspectives of energy use, carbon emissions, the macroeconomy, and institutional income. The simulation results indicate that to achieve the goal of carbon peaking by 2030, the annual installed capacity of nonfossil energy must reach 112.29 gigawatts, and average annual coal consumption in the China 15th Five-Year Plan and 16th Five-Year Plan should be reduced by 20 million and 40 million tons, respectively, which will result in the proportion of nonfossil energy in primary energy consumption reaching about 25%. Limiting coal consumption will slow economic growth, whereas increasing the installed capacity of nonfossil energy will stimulate economic growth. The combined policies will have a significant impact on reducing carbon emissions and achieving the carbon peaking goal and will also offset the adverse effects of such policies on the macroeconomy.

Predicting structural changes of the energy sector in an input–output framework

The share of renewable energies has to increase significantly in the ongoing energy transition. Such a shift in production technology is expected to have noticeable effects on the energy sector’s input structure that is required for its output. This study examines how changes in a country’s energy mix affect its energy sector’s input coefficients within an input–output framework, using Austria’s renewable expansion act as a case study. Predicting input coefficients can be time-consuming and often relies on trends in past data. Our empirical approach is based on a fractional econometric model using panel data on the energy mix and input structures of energy sectors for 26 European countries, and can be efficiently and readily applied to the 26 countries covered in the model. We illustrate the prediction of input coefficients for Austria’s energy sector in 2030. We find that input shares from the energy sector to itself would remain high, while mining inputs would decrease. Our model also predicts that increasing the share of renewable energy sources comes with a significant decrease in the share of labor inputs, mainly because operating renewable energy technologies requires less labor than operating non-renewable ones. The presented method allows to assess renewable energy policy plans to anticipate the effects of structural changes in national energy sectors.

A Review of Future Fuel Cell Electric Vehicles and Challenges Related to Morocco

According to estimates from Madrid, Paris and Berlin, Morocco wants to provide Europe with substantial amounts of solar energy and green hydrogen in the future, paving the way for climate neutrality. Morocco is a leader in climate and energy policy in Africa, as well as in the rest of the world. The Maghreb state is pursuing aggressive CO2 reduction targets and has been a major participant in international climate talks, hosting COP22 in Marrakech in 2016. By the end of 2020, the country had built just over 40 percent renewable capacity, and this is expected to reach 52 percent by 2030. Morocco's energy policy plan has now added an ambitious new goal: it aspires to become the global market leader in green hydrogen production. With the growing demand for this new zero-emission fuel, hydrogen manufacturing is a solid bet for the future. In addition, the Kingdom has set ambitious targets for reducing CO2 emissions and integrating electric vehicles as the main solution to reach the 2030 targets. This paper aims to provide a better understanding of fuel cell electric vehicles as well as explore the future of FCEVs in Morocco through an in-depth analysis of the Moroccan hydrogen roadmap. In addition, a SWOT analysis was detailed to determine the key success factor to encourage the adoption of FCEVs in the Kingdom. In the same sense, this paper represents an overview of electric vehicles established for the future realization of prototype FCEVs by our team, this through the integration of the fuel cell in a solar electric vehicle, possibly providing a hybrid power system.

Can Taiwan's “2025 Non-Nuclear Homeland” policy achieve the expected carbon emission reduction goals?

Taiwan's per capita carbon emissions ranks first in Asia. Whether Taiwan's current “2025 Non-Nuclear Homeland” energy transition policy can achieve the goal of greenhouse gas reduction is an issue that needs to be studied urgently. On the basis of studying the status quo of energy supply and demand and carbon emissions in Taiwan, this paper first uses an econometric model to predict Taiwan's energy demand from 2021 to 2030, and then uses a Markov chain model to predict its energy structure changes and CO2 emission trends under different scenarios, and evaluates the impact of its energy transition policies on carbon emission reduction goals. On this basis, by comparing the carbon trading markets on both sides of the Taiwan strait, the feasibility of and obstacles to the cross-strait cooperation in the carbon trading market are discussed in depth, and the opportunities for future cross-strait cooperation in the carbon trading market are proposed innovatively. The empirical results of this paper show that the carbon emissions in 2025 under the energy transition policy will be reduced by 59.8–61.1 million tons compared with those without planning constraints, but compared with the carbon reduction target (a reduction of 124.8–133 million tons), there is still a long way to go. The contribution potential of the annual carbon emission reduction target is only 45.9%–47.9%, and the contribution potential in 2030 is 57.2%–60.1%. The current “2025 Non-Nuclear Homeland” policy is not in line with Taiwan's current reality, and it is difficult to achieve the expected carbon reduction goal. In order to realize the energy transition in Taiwan in the future, it is necessary to make overall planning of energy policies, explore the cross-strait power cooperation model, and gradually try to build a cross-strait carbon trading market.

Strategic energy and climate policy planning: Lessons learned from European energy efficiency policies

Strategic policy planning has gained increasing importance in international energy and climate policy. This paper aims to contribute to the debate on stringent policy planning by drawing lessons learned from two decades of strategy development in the field of European energy efficiency policy. Since the inception of the first national energy efficiency action plans under the Energy Services Directive (2006/32/EC), the European Commission and the Member States have had a long experience in planning national strategies. This paper analyses the lessons learned in setting up national energy efficiency strategies and traces the progress made in 2007–2020. Our findings show that major improvements are attained in the latest national energy and climate plans through the adoption of harmonised reporting approaches, evidence-based target setting methods, establishment of better monitoring systems and broader consideration of policy packages. Various areas with a significant potential for further improvements are also identified, ranging from more coherent reporting framework for policies and measures to the need of setting up systems that encourage the adoption of targets in line with a country's cost-effective energy efficiency potential. Embedding energy efficiency in the larger climate policy framework, considering how it can support pressing socioeconomic challenges, is also of pivotal importance.

Energy security in the external energy policy of the USA

Energy security is a key idea of countries’ energy policy at both domestic and international levels. The aim of the paper is to reveal and describe the role and significance of the concept of energy security within the external energy policy of the USA with particular attention to its regional aspects. In a theoretical section, a number of definitions of energy security were discussed and summarized, presenting a brief theoretical overview of energy security. Besides, the need for a spatially diversified approach towards energy policy planning and implementation was described briefly. Analyzing relevant secondary data sources (Department of Energy and Congressional Research Service publications, energy statistics, etc.), the main priorities of U.S. external energy policy were identified and discussed: international assistance and capacity building, environmentally friendly energy development and ensuring energy security (of both the USA and its international allies and partners). Thus, the general framework of U.S. external energy policy was suggested and generalized. As a regional case overview, the main issues of European energy security and U.S. engagement were discussed in brief: geographical diversification of energy supply, legislative and institutional assistance, etc.

Preliminary evaluation on specific energy consumption of refrigerated trucks in Thailand’s cold chain used for national energy policy planning

The objective of this study was to evaluate the suggested specific energy consumption (SEC) of refrigerated trucks in the cold chain in Thailand. Types of refrigerated trucks were classified following the regulations of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), the Ministry of Public Health, Thailand and the Department of Land Transport, the Ministry of Transport, Thailand. The fuel consumption, weight, distance traveled, and other data were recorded on 50 refrigerated trucks operating in practice. The SEC was calculated following the European Standard EN 16212:2012. The relationship between the SEC and the ratio between good weight and maximum allowance load weight or called as the weight ratio for each truck type was established in a power function. The suggested SEC for each truck type at a certain weight ratio could be determined from this relationship. The lowest SEC or the target for the best energy saving practice was determined at the weight ratio of 1.0. In addition, the comparison found that the international small-sized truck had lower SEC than the 4-wheeler truck in Thailand. Therefore, the domestic 4-wheeler refrigerated trucks should be the primary target for the government to enforce the cold chain energy saving plan.