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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 37124

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Special Issue Editors

Prof. Dr. Zhengxin Wang

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Guest Editor

School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Interests: economic forecasting and policy evaluation; energy economics; grey system theory; time series forecasting in various fields, such as energy systems, industrial systems, and environmental management

Dr. Song Ding

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Guest Editor

School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Interests: energy economics; environmental management; econometric modelling; time series forecasting in fields of energy, environment, and economy

Dr. Xin Ma

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Guest Editor

School of Science, Southwest University of Science and Technology, Mianyang 621010, China
Interests: grey system; machine learning; energy prediction
Special Issues, Collections and Topics in MDPI journals

Dr. Wendong Yang

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Guest Editor

1 School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China
2 Institute of Marine Economics and Management, Shandong University of Finance and Economics, Jinan 250014, China
Interests: artificial intelligence; big data; machine learning; data mining in energy; economics and environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As governments and the general public become more keenly aware of the critical issues arising from how humans use their environment and energy, this Special Issue provides a forum for the discussion and analysis of environmental and energy problems around the world and for the presentation of management results. It is aimed not only at the environmental and energy managers, but at anyone concerned with the sustainable use of environmental and energy resources. Moreover, under the current background of global environmental degradation and energy shortage, it is more imperative to promote the development of environmental and energy economics, providing theoretical and practical support for decision and policy makers. Therefore, investigations on the environmental and energy economics topics will be conducted in Sustainability.

Contributions to this Special Issue are expected to bring new knowledge and insights to environmental and energy economics. Themes include but are not limited to the following areas:

Development of the traditional topics on environmental and energy economics (exploitation, conversion, and use of energy, environmental and energy policy, regulation and taxation, international trade, monetary policy, etc.);
Development of emerging topics on environmental and energy economics (clean energy, carbon finance, green production and consumption policy system, markets for energy commodities and derivatives, etc.);
Methods and approaches of the environment and energy economics, including but not limited to econometric models, grey system models, fuzzy theory, machine learning, artificial neural networks, deep learning, and other advanced analytical models;
Environmental and energy-related analysis and assessment (risk assessment, development trends analysis, life cycle analysis, life cycle costing, emission accounting and forecasting, etc.);
Social, economic and policy aspects of environmental and energy systems.

Prof. Dr. Zhengxin Wang
Dr. Song Ding
Dr. Xin Ma
Dr. Wendong Yang
Guest Editors

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Keywords

environmental and energy economics
energy production and consumption
regulation and taxation
carbon emission reduction
clean energy
carbon finance and trade
environmental and energy policy
green production and consumption policy
econometric models
grey system models
machine learning
artificial neural networks
environmental and energy forecast and analysis
sustainable development

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Published Papers (14 papers)

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26 pages, 3464 KiB

Open AccessArticle

Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States

by Xin Ma, Yubin Cai, Hong Yuan and Yanqiao Deng

Sustainability 2023, 15(9), 7086; https://doi.org/10.3390/su15097086 - 23 Apr 2023

Cited by 2 | Viewed by 1496

Abstract

Energy forecasting based on univariate time series has long been a challenge in energy engineering and has become one of the most popular tasks in data analytics. In order to take advantage of the characteristics of observed data, a partially linear model is proposed based on principal component analysis and support vector machine methods. The principal linear components of the input with lower dimensions are used as the linear part, while the nonlinear part is expressed by the kernel function. The primal-dual method is used to construct the convex optimization problem for the proposed model, and the sequential minimization optimization algorithm is used to train the model with global convergence. The univariate forecasting scheme is designed to forecast the primary energy consumption of the electric power sector of the United States using real-world data sets ranging from January 1973 to January 2020, and the model is compared with eight commonly used machine learning models as well as the linear auto-regressive model. Comprehensive comparisons with multiple evaluation criteria (including 19 metrics) show that the proposed model outperforms all other models in all scenarios of mid-/long-term forecasting, indicating its high potential in primary energy consumption forecasting. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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20 pages, 3520 KiB

Open AccessArticle

Forecasting Chinese Electricity Consumption Based on Grey Seasonal Model with New Information Priority

by Weijie Zhou, Huihui Tao, Jiaxin Chang, Huimin Jiang and Li Chen

Sustainability 2023, 15(4), 3521; https://doi.org/10.3390/su15043521 - 14 Feb 2023

Cited by 3 | Viewed by 1607

Abstract

The total electricity consumption in China includes almost all the electricity consumption from all fields, which can reflect the overall situation of China’s electricity consumption, and it is of great significance to forecast it. This paper develops a novel grey Holt-Winters model based on the new information priority cycle accumulation operator, known as the NCGHW model for short, in order to effectively forecast the total electricity consumption in China. First of all, this paper proposes the new information priority cycle accumulation operator to mine the internal law of data while maintaining periodicity in the accumulated data. Then, based on the one-order accumulation sequence generated by the new operator, the framework of the Holt-Winters model is used to build a new model. Finally, according to the characteristics of the data itself, the LBFGS algorithm is used to find the most suitable parameters for the model. In order to model and analyze the fine-grained measurement of the total electricity consumption in China, we study the monthly and quarterly data, respectively. The new model and the contrast models are applied to the two sequences for simulation and prediction. The performance of the model is discussed through relevant evaluation criteria. The results show that the new model has sufficient capacity to forecast the monthly and quarterly total electricity consumption. It is the best choice for the total electricity consumption in China. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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17 pages, 2145 KiB

Open AccessArticle

Human Capital and Carbon Emissions: The Way forward Reducing Environmental Degradation

by AM Priyangani Adikari, Haiyun Liu, DMSLB Dissanayake and Manjula Ranagalage

Sustainability 2023, 15(4), 2926; https://doi.org/10.3390/su15042926 - 6 Feb 2023

Cited by 16 | Viewed by 2415

Abstract

Many environmental problems are human induced, one of which is the change in atmospheric composition, a hot research topic in recent decades. This study aims to investigate the impact of human capital (HC) on carbon dioxide (CO₂) emissions in Sri Lanka using time series annual data from 1978 to 2019. The time series data were examined for a unit root problem and an unknown structural break. An autoregressive distributed lag (ARDL) approach was employed to identify the long-run relationship between HC and CO₂. The results confirm the long-term relationship between carbon emissions and human capital. As a unique finding of this research, the estimated coefficient of human capital to carbon emission is negative and statically significant, suggesting that a 1 percent increase in HC decreases carbon emissions by 1.627789 percent. The significance of this finding is that it can help achieve Sustainable Development Goal “13”, which focuses on combating climate change and its effects. The study indicated that building in HC by investing more in education helps to reduce carbon emissions in the long term. It reflects that human capital accumulation is linked to reduced environmental degradation due to lower CO₂ emissions. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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21 pages, 1443 KiB

Open AccessArticle

Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model

by Yifei Chen and Zhihan Fu

Sustainability 2023, 15(3), 1895; https://doi.org/10.3390/su15031895 - 19 Jan 2023

Cited by 6 | Viewed by 3241

Abstract

COVID-19 has continuously influenced energy security and caused an enormous impact on human life and social activities due to the stay-at-home orders. After the Omicron wave, the economy and the energy system are gradually recovering, but uncertainty remains due to the virus mutations that could arise. Accurate forecasting of the energy consumed by the residential and commercial sectors is challenging for efficient emergency management and policy-making. Affected by geographical location and long-term evolution, the time series of the energy consumed by the residential and commercial sectors has prominent temporal and spatial characteristics. A hybrid model (CNN-BiLSTM) based on a convolution neural network (CNN) and bidirectional long short-term memory (BiLSTM) is proposed to extract the time series features, where the spatial features of the time series are captured by the CNN layer, and the temporal features are extracted by the BiLSTM layer. Then, the recursive multi-step ahead forecasting strategy is designed for multi-step ahead forecasting, and the grid search is employed to tune the model hyperparameters. Four cases of 24-step ahead forecasting of the energy consumed by the residential and commercial sectors in the United States are given to evaluate the performance of the proposed model, in comparison with 4 deep learning models and 6 popular machine learning models based on 12 evaluation metrics. Results show that CNN-BiLSTM outperforms all other models in four cases, with MAPEs ranging from 4.0034% to 5.4774%, improved from 0.1252% to 49.1410%, compared with other models, which is also about 5 times lower than that of the CNN and 5.9559% lower than the BiLSTM on average. It is evident that the proposed CNN-BiLSTM has improved the prediction accuracy of the CNN and BiLSTM and has great potential in forecasting the energy consumed by the residential and commercial sectors. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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14 pages, 1076 KiB

Open AccessArticle

Factors Affecting Environmental Pollution for Sustainable Development Goals—Evidence from Asian Countries

by Nguyen Thi Phuong Thu, Le Mai Huong and Vu Ngoc Xuan

Sustainability 2022, 14(24), 16775; https://doi.org/10.3390/su142416775 - 14 Dec 2022

Cited by 11 | Viewed by 4614

Abstract

The world is faced with climate change and gradual increases in seawater and carbon dioxide levels, and leaders of countries all over the world need to take action in order to achieve the Sustainable Development Goals (SDGs). This paper aims to identify the factors affecting environmental pollution in Asian countries for sustainable development. This study collected data from the World Bank covering 2000–2020 for 15 Asian countries. The data were processed via STATA 17.0; the study employed the unrestricted fixed effect to solve the research problems. The empirical results suggest that electricity consumption, fossil fuel consumption, renewable consumption, population, imports, and exports affected environmental pollution in the 15 Asian countries. In addition, electricity consumption and fossil fuel consumption had a strong positive effect on Asia’s environmental pollution. Moreover, population and renewable consumption negatively affected CO₂ emissions. These results indicate that, if an Asian country’s electricity consumption increases by 1%, then its CO₂ emissions will increase by 0.674%; if an Asian country’s fossil fuel consumption increases by 1%, then its CO₂ emissions will increase by 0.203%; if an Asian country’s renewable consumption increases by 1%, then its CO₂ emissions will decrease by 0.01568%; if an Asian country’s export of goods and services increases by 1%, then its CO₂ emissions will decrease by 0.054%; if an Asian country’s import of goods and services increases by 1%, then its CO₂ emissions will increase by 0.067%; if an Asian country’s population increases by 1%, then its CO₂ emissions will decrease by 0.2586%. Based on the empirical results, the study suggests new policies for green energy to achieve the Sustainable Development Goals (SDGs). Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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23 pages, 1755 KiB

Open AccessArticle

Dynamic Evaluation and Regional Differences Analysis of the NEV Industry Development in China

by Yanhua Liang and Hongjuan Lu

Sustainability 2022, 14(21), 13864; https://doi.org/10.3390/su142113864 - 25 Oct 2022

Cited by 2 | Viewed by 1901

Abstract

In the transportation sector, new energy vehicles (NEVs) are critical to reduce CO₂ emissions in the context of carbon neutralization. The study of dynamic evaluation and regional difference analysis is helpful to the NEV industry development in policy design and industrial planning. In this study, based on the provincial data in China from 2016 to 2020, the grey target model and Dagum Gini coefficient method are employed for the dynamic evaluation and regional differences of the NEV industry development. The results were as follows: (1) The overall and provincial level of the NEV industry development showed an increasing pattern. The bull’s eye degrees of Guangdong, which had the best development, were 0.4884, 0.5361, 0.6067, 0.6787, and 0.7047 during the study period. (2) The regional differences in the NEV industry development were significant. The east region had the best development, followed by the middle, the west, and the northeast regions. The intra-regional differences were expanding with different annual growth rates. (3) The inter-regional differences between the east and the other three regions were the largest. The regional differences in the NEV development are mainly derived from inter-regional dereference. (4) The D₁, D₂, and D₃ dimensions all contributed significantly to provinces with higher levels of development, while the D₄ dimension contributed significantly to provinces with lower levels of development. Based on these results, different provinces should take differentiated development strategies and enhancement paths to promote their NEV industry development. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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18 pages, 2097 KiB

Open AccessArticle

Does Environmental Regulation Improve Carbon Emission Efficiency? Inspection of Panel Data from Inter-Provincial Provinces in China

by Pan Jiang, Mengyue Li, Yuting Zhao, Xiujuan Gong, Ruifeng Jin, Yuhan Zhang, Xue Li and Liang Liu

Sustainability 2022, 14(16), 10448; https://doi.org/10.3390/su141610448 - 22 Aug 2022

Cited by 17 | Viewed by 2245

Abstract

This study aims to analyze the nonlinear relationship between environmental regulation and carbon emission efficiency and provide scientific reference for achieving the goal for carbon neutrality at a lower cost. Taking 30 provinces in China, using dual carbon policy as the research objects, the slacks-based measure–Malmquist–Luenberger (SBM–ML) index method was used to measure the carbon emission efficiency from 2009 to 2019 and a panel threshold regression model was established to explore the nonlinear effects of environmental regulation and carbon emission efficiency in each province. The results show that: (1) during the sample period, there is geographical variability in CEE, with the eastern coastal provinces having the highest CEE, followed by the central and western provinces, and the resource-dependent provinces having the lowest CEE and their energy consumption and utilization efficiency being significantly lower than other provinces; (2) when the energy consumption intensity is used as a threshold variable, the relationship between environmental regulation and carbon emission rate is an inverted “U” shape; and (3) when green technology innovation is used as a threshold variable, the relationship between environmental regulation and carbon emission rate is a “U” shape. This study provides a new perspective for improving carbon emission efficiency. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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14 pages, 346 KiB

Open AccessArticle

Education, Financial Development, and Primary Energy Consumption: An Empirical Analysis for BRICS Economies

by Gamze Sart, Mehmet Hilmi Ozkaya and Yilmaz Bayar

Sustainability 2022, 14(12), 7377; https://doi.org/10.3390/su14127377 - 16 Jun 2022

Cited by 5 | Viewed by 1884

Abstract

Energy is life blood of all economies and an indispensable prerequisite for all economic activities and consequently factors influencing the energy consumption are of vital importance. Therefore, this study investigates the effect of education together with financial development on energy consumption in sample of BRICS economies over the 1990–2019 period by means of second-generation cointegration and causality analyses thanks to the small number of empirical studies. The causality analysis unveils a one-way causal effect from education to primary energy consumption, but an insignificant causality between financial development and primary energy consumption. The cointegration analysis uncovers a strong positive effect of education at panel level and in all BRICS economies in the long-term, but financial sector development has a significant positive influence on primary energy use only in South Africa in the long-term. The findings of the study reveal that education considerably increases the primary energy use in the BRICS economies through economic growth channel, but financial sector development has not been a significant determinant of primary energy use yet. However, the BRICS economies should attach more importance to green technology and energy focused growth for sustainable growth and development. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

20 pages, 3281 KiB

Open AccessArticle

Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction

by Guosheng Duan, Lifeng Wu, Fa Liu, Yicheng Wang and Shaofei Wu

Sustainability 2022, 14(11), 6824; https://doi.org/10.3390/su14116824 - 2 Jun 2022

Cited by 5 | Viewed by 1847

Abstract

Accurate forecasting of solar radiation (Rs) is significant to photovoltaic power generation and agricultural management. The National Centers for Environmental Prediction (NECP) has released its latest Global Ensemble Forecast System version 12 (GEFSv12) prediction product; however, the capability of this numerical weather product for Rs forecasting has not been evaluated. This study intends to establish a coupling algorithm based on a bat algorithm (BA) and Kernel-based nonlinear extension of Arps decline (KNEA) for post-processing 1–3 d ahead Rs forecasting based on the GEFSv12 in Xinjiang of China. The new model also compares two empirical statistical methods, which were quantile mapping (QM) and Equiratio cumulative distribution function matching (EDCDFm), and compares six machine-learning methods, e.g., long-short term memory (LSTM), support vector machine (SVM), XGBoost, KNEA, BA-SVM, BA-XGBoost. The results show that the accuracy of forecasting Rs from all of the models decreases with the extension of the forecast period. Compared with the GEFS raw Rs data over the four stations, the RMSE and MAE of QM and EDCDFm models decreased by 20% and 15%, respectively. In addition, the BA-KNEA model was superior to the GEFSv12 raw Rs data and other post-processing methods, with R² = 0.782–0.829, RMSE = 3.240–3.685 MJ m⁻² d⁻¹, MAE = 2.465–2.799 MJ m⁻² d⁻¹, and NRMSE = 0.152–0.173. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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18 pages, 2007 KiB

Open AccessArticle

Application of a Novel Optimized Fractional Grey Holt-Winters Model in Energy Forecasting

by Weijie Zhou, Huihui Tao and Huimin Jiang

Sustainability 2022, 14(5), 3118; https://doi.org/10.3390/su14053118 - 7 Mar 2022

Cited by 12 | Viewed by 2339

Abstract

It is of great significance to be able to accurately predict the time series of energy data. In this paper, based on the seasonal and nonlinear characteristics of monthly and quarterly energy time series, a new optimized fractional grey Holt–Winters model (NOFGHW) is proposed to improve the identification of the model by integrating the processing methods of the two characteristics. The model consists of three parts. Firstly, a new fractional periodic accumulation operator is proposed, which preserves the periodic fluctuation of data after accumulation. Secondly, the new operator is introduced into the Holt–Winters model to describe the seasonality of the sequence. Finally, the LBFGS algorithm is used to optimize the parameters of the model, which can deal with nonlinear characteristics in the sequence. Furthermore, in order to verify the superiority of the model in energy prediction, the new model is applied to two cases with different seasonal, different cycle, and different energy types, namely monthly crude oil production and quarterly industrial electricity consumption. The experimental results show that the new model can be used to predict monthly and quarterly energy time series, which is better than the OGHW, SNGBM, SARIMA, LSSVR, and BPNN models. Based on this, the new model demonstrates reliability in energy prediction. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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14 pages, 697 KiB

Open AccessArticle

Impacts of Energy Price on Agricultural Production, Energy Consumption, and Carbon Emission in China: A Price Endogenous Partial Equilibrium Model Analysis

by Yongxi Ma, Lu Zhang, Shixiong Song and Shuao Yu

Sustainability 2022, 14(5), 3002; https://doi.org/10.3390/su14053002 - 4 Mar 2022

Cited by 15 | Viewed by 3929

Abstract

Energy market volatility will have systemic effects on agricultural production, energy consumption and carbon emissions. This paper aims to evaluate the impacts of energy price on agricultural production, energy consumption, and carbon emission in China. To achieve the objective, this paper, firstly, constructed a price endogenous partial equilibrium model, and then designed four scenarios of energy price fluctuations, finally evaluating the impacts of energy price fluctuations on agricultural production and its energy consumption and carbon emission. The results revealed that: (1) The impacts on agricultural production are very limited, but higher energy price will result in producers’ welfare loss by 0.6% to 1.4%, under different scenarios. (2) Energy price drives negative impacts on agricultural energy consumption and carbon emission, 1.6%/3.2% and 1.3%/2.6%, respectively, in low/high amplitude scenarios. (3) Heterogeneous impacts are confirmed in the regional analysis; South China is simulated to be the most sensitive area. To mitigate the impacts from energy price and reduce carbon emission in agriculture, several policy implications have recently been proposed, including strengthening supervision of the energy market, constructing an energy saving price-setting mechanism, launching policy instruments to improve energy efficiencies and facilitate cleaner farming techniques, and formulating specific measurements of energy saving and emission reduction for different regions. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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14 pages, 9698 KiB

Open AccessArticle

A Novel Model Based on DA-RNN Network and Skip Gated Recurrent Neural Network for Periodic Time Series Forecasting

by Bingqing Huang, Haonan Zheng, Xinbo Guo, Yi Yang and Ximing Liu

Sustainability 2022, 14(1), 326; https://doi.org/10.3390/su14010326 - 29 Dec 2021

Cited by 11 | Viewed by 2373

Abstract

Deep learning models are playing an increasingly important role in time series forecasting with their excellent predictive ability and the convenience of not requiring complex feature engineering. However, the existing deep learning models still have shortcomings in dealing with periodic and long-distance dependent sequences, which lead to unsatisfactory forecasting performance on this type of dataset. To handle these two issues better, this paper proposes a novel periodic time series forecasting model based on DA-RNN, called DA-SKIP. Using the idea of task decomposition, the novel model, based on DA-RNN, GRU-SKIP and autoregressive component, breaks down the prediction of periodic time series into three parts: linear forecasting, nonlinear forecasting and periodic forecasting. The results of the experiments on Solar Energy, Electricity Consumption and Air Quality datasets show that the proposed model outperforms the three comparison models in capturing periodicity and long-distance dependence features of sequences. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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0 pages, 2485 KiB

Open AccessArticle

RETRACTED: The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

by Shu Wu

Sustainability 2021, 13(24), 13859; https://doi.org/10.3390/su132413859 - 15 Dec 2021

Cited by 3 | Viewed by 2191 | Retraction

Abstract

As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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Review

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28 pages, 1617 KiB

Open AccessReview

Key Processes for the Energy Use of Biomass in Rural Sectors of Latin America

by Emerita Delgado-Plaza, Artemio Carrillo, Hugo Valdés, Norberto Odobez, Juan Peralta-Jaramillo, Daniela Jaramillo, José Reinoso-Tigre, Victor Nuñez, Juan Garcia, Carmina Reyes-Plascencia, Nestor Tancredi, Franco Gallardo, Ivan Merino, Gabriel León, José Torres, Carlos Garcia and Ian Sosa-Tinoco

Sustainability 2023, 15(1), 169; https://doi.org/10.3390/su15010169 - 22 Dec 2022

Cited by 5 | Viewed by 2853

Abstract

An alternative to mitigate the consumption of fossil fuels is the use of biomass as an energy source. In this sense, the rural sector in Latin America has great potential due to its multiple biomass sources. For this reason, this study aims to analyze potential technologies related to the production of energy from biomass and its application in the Latin American rural sector. To achieve this, four key processes are analyzed. First is biomass conditioning through solar dryers. Next are the thermochemical processes that allow for their transformation into biofuels, for which the pyrolysis and the hydrothermal methods were selected due to the flexibility of the products obtained. Subsequently, cogeneration is studied to produce electrical and thermal energy from biomass or its derivatives. Finally, to close the CO₂ cycle, a balance of CO₂ fixation in a forest plantation is presented as an example of carbon accumulated in biomass. The literature systematic review allowed us to determine that the technologies mentioned in this work have different degrees of implementation in the Latin American rural sector. However, they have great potential to be applied on a large scale in the region, making it possible to adapt energy production to climate change and improve the life quality of its inhabitants. Full article

(This article belongs to the Special Issue Development Trends of Environmental and Energy Economics)

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