Non-renewable Energy Consumption Research Articles

Synergizing social sustainability and technological innovation for carbon neutrality in OECD economies

AbstractEconomies around the globe are pushing toward a society's sustainability perspectives and upgradation in technology to protect the environment from global warming and the depletion of energy supplies. This shift in perception is primarily intended to alter people's attitudes toward posterity. By integrating the theories of interactions between social and ecological systems and the environment Kuznets curve (EKC) framework, this study explores the impact of nonrenewable energy consumption, social sustainability, and technological innovation on CO₂ emissions across the Organization for Economic Co‐operation and Development (OECD) countries from 2001 through 2020. Based on the estimates of FMOLS, PARDL, and QARDL, the 10‐year CO2 emissions and gross domestic product projections are traced using the average growth rate for each country and an average value of independent variables. The findings of this study highlight the detrimental effect of nonrenewable energy consumption on CO₂ emissions, underscoring the urgency for sustainable energy alternatives. However, the positive role of social sustainability and technological innovation in curbing CO₂ emissions within OECD countries is equally significant. Accentuating the importance of social sustainability within this context, the study sheds light on the integral role that societal factors, such as education, public awareness, and social well‐being, play a substantial role in shaping sustainable practices. It is found that Brazil, China, Malaysia, Russia, South Africa, and Thailand need to focus on higher economic growth to experience decarbonizing economic growth.

The Impact of Fossil Fuel Energy, Technological Innovation and Globalization on Tourism Inflows of Top Ten Most Visited Countries

Increased uses of fossil fuel consumption, globalization, and tourism activities have significant adverse effects on the sustainability of our ecosystem. The present study takes into account the top 10 most visited nations by tourists to examine the impact of fossil fuel energy consumption, technological innovation, foreign direct investment (FDI), economic growth, and globalization on tourism inflows from 2008 to 2022.. Choice of appropriate technique for empirical testing is based on the PP-Fisher, ADF Fisher, IPS, and LLC unit root tests. Due to mixed order of integration of variables, the Panel ARDL is more reliable for econometric investigation. The findings of Panel ARDL reveal that the most significant contributing factors towards growth in tourism are technological innovation, the globalization index, growth in gross domestic product, and use of fossil fuels as an energy source. These results imply that advancements in technology, increased globalization trends, and economic growth are facilitating the growth in tourism activities. Likewise, non-renewable energy consumption is significantly linked to tourism growth in these countries which exhibits that these governments are relying upon fossil fuel consumption to meet the energy demand for tourism activities and to fuel economic growth. Though, FDI is having a detrimental effect on tourism inflows in the top 10 most frequently visited nations. This outcome entails that expanded industrialization and economic footprint negatively influence the ecological environment which adversely affects tourism inflows. The policy implications of this research assert that the development of clean and renewable energy technologies is essential for the promotion of sustainable economic growth, promote eco-tourism in these countries. Similarly, smart manufacturing and environment friendly production can alleviate the negative effects of ecological footprint on tourism inflows.

Does energy transition reduce carbon inequality? A global analysis.

Energy transition from fossil fuels to renewables is instrumental in mitigating climate change. Low-income countries have a higher share of renewable energy in their total energy consumption than rich countries (WDI, 2023). Thus, it is imperative to examine the role of energy transition in affecting relative CO2 emissions between rich and poor sections of the societies across income groups of the countries. In this context, our study contributes by constructing the carbon inequality models with renewable and non-renewable energy consumption as prime explanatory variables separately for 114 countries over a data period 1990-2019. The models are estimated individually for high-middle-low-income countries by controlling for foreign direct investment (FDI), economic growth, and innovations. Starting with preliminary econometric operations, we employ the dynamic simulated panel autoregressive distributed lag approach and Driscoll-Kraay standard error regression for empirical investigation. We find that energy transition reduces carbon inequality globally. Innovation has a negative impact, economic growth has a positive impact on carbon inequality, and FDI has an asymmetric impact based on the income level of the countries. The crucial global policy implications are discussed.

Do Increases and Decreases in Non-renewable Energy Consumption Have the Same Effect on Growth in Türkiye?

This study examines the relationship between fossil energy consumption (FEC) and economic growth by applying the non-linear ARDL method in the Türkiye sample. This relationship was addressed in 3 different models to eliminate the multicollinearity between the oil, natural gas, and coal variables that make up the FEC. According to the analysis results, all models have an asymmetric cointegration between the variables. In all models, the effect of decreases in energy consumption on economic growth is more dominant than increases in the long run. According to the causality results, the neutrality hypothesis is valid for coal consumption, the feedback hypothesis is valid for natural gas consumption, and the growth hypothesis is valid for oil consumption.

The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries

Impacts of renewable and disaggregated non-renewable energy consumption on CO2 emissions in GCC countries: A STIRPAT model analysis

This research investigates the effects of renewable (REC) and disaggregated non-renewable energy consumption (coal, oil, and natural gas) on CO2 emissions (CO2) in GCC countries, employing the STIRPAT model. The research also compares the impact of various non-renewable energy (NREC) sources to identify their contributions to CO2 emissions. Demographic factors like population and economic growth are considered main determinants of CO2. Panel data econometric methods are used, including diagnostic tests and unit root tests, to found long-run relationships among the variables. The study reveals significant positive associations between coal, natural gas, oil consumption and CO2, with oil having the highest impact. Conversely, REC shows a significant negative correlation with CO2. Economic growth and population are also linked to increased CO2. The findings emphasize the need for strategies promoting renewable energy usage, energy efficiency, public transportation, carbon pricing, and research in green technologies to alleviate CO2 and enhance sustainable development in the GCC countries.

Design and assessment of energy infrastructure in new decarbonized urban districts: A Spanish case study

New urban districts have great potential to reduce greenhouse gas emissions by electrifying energy demand in various sectors, such as transportation and buildings. This paper proposes a novel approach that combines different energy models to plan the infrastructure for supplying the energy demand of new, decarbonized, and highly electrified urban districts on a real-world scale. First, a model for energy management in buildings is used to plan and operate equipment for heating, cooling, and solar photovoltaic distributed generation. Second, a model that plans electric vehicle charging infrastructure determines the number and types of charging stations. Then, a large-scale distribution network planning model designs a cost-efficient electricity distribution grid to supply the district. A case study is presented for a new urban district in Madrid, Spain, to demonstrate how this approach can be applied to energy infrastructure planning in a real-world context. This case study presents different scenarios of energy efficiency performances in buildings and penetration levels of solar installations, electric vehicles, and heating and cooling systems. The results show that even in a high electrification scenario, improving energy efficiency in buildings through a district heating network based on heat pumps can lead to a lower peak electricity demand. This peak load reduction allows for integrating more electric vehicles, avoiding further investments in the electricity distribution network. In addition, the results confirm that a highly electrified scenario, which combines energy-efficient buildings with high integration of solar and electric vehicles, significantly reduces non-renewable energy consumption and greenhouse gas emissions.

Study on the synergistic effects and eco-friendly performance of red mud-based quaternary cementitious materials

Red mud, as the primary byproduct of processing bauxite into aluminum, raises environmental concerns due to its significant alkalinity and substantial heavy metal content. Addressing the limitations of traditional methods, this study proposes an innovative solution to utilize red mud, steel slag, fly ash, and phosphogypsum together to produce a new type of low-carbon, environmentally friendly cementitious material. Utilizing advanced techniques such as X-ray diffraction, thermogravimetric analysis, Fourier-transform infrared spectroscopy, mercury intrusion porosimetry, and scanning electron microscopy with energy-dispersive spectroscopy, this study conducted a comprehensive evaluation of cementitious materials, revealing their synergistic mechanisms. Furthermore, artificial neural networks and genetic algorithms were employed to optimize the mix ratio of the quaternary binder, aiming to predict and enhance the material's mechanical strength. Research shows that quaternary cementitious materials outperform traditional binary and ternary materials in compressive strength, hydration properties, microstructure, and environmental performance. Particularly, the increased content of calcium-aluminate-silicate-hydrate and sodium-calcium-silicate-aluminate-hydrate gels and a denser matrix structure highlight the synergistic effect in the hydration reaction of the quaternary system. Through mix proportion optimization, a significant enhancement in compressive strength up to 21.4 MPa is achieved. Environmental assessment shows that the composite material effectively solidifies Na+, with non-renewable resource and energy consumption reduced by 84.7% and 83.8%, respectively, compared to traditional cement, highlighting its environmental and energy-saving advantages. This approach addresses red mud storage, produces green building materials that meet standards, and fulfills resource and environmental goals.

Integrating health, nutrition, and environmental impacts of foods: a life cycle impact assessment and modelling analysis of foods in Canada

BackgroundGiven the urgency of transitioning towards sustainable nutrition, dietary shifts that provide co-benefits to human health and the environment are imperative. There is currently no database of the environmental impacts of foods that reflects Canada's unique geographical and agri-climatic context and regional inputs and emissions. To determine sustainable diets, harmonising nutritional considerations with environmental impacts is also essential for an equitable comparison of foods. We aimed to develop a Canadian Food Life Cycle Inventory database and a multidimensional index to enable a joint assessment of the health and environmental impacts of foods in Canada. MethodsThe Canadian Food Life Cycle Inventory database uses life cycle assessment methodology to evaluate environmental impacts. The datasets mirror Canada's food consumption patterns, averaging the spectrum of agricultural practices weighted by domestic production and import shares. The database is structured according to the nomenclature and categorisation of the Canadian Nutrient File. Environmental sustainability is assessed using a cradle-to-grave approach, including indicators such as greenhouse gas emissions, eutrophication, particulate matter, freshwater usage, land use, non-renewable energy consumption, and food loss and waste. Environmental impacts are quantified through an environmental impact score (EIS) assigned to each impact category for a given food. The EIS-nutrition (EIS-N) integrates the evaluation of nutritional quality with environmental impacts using Nutri-Score, a validated food nutrient-profiling tool. The EIS-N is modelled as a ratio of the EIS to the Nutri-Score values. FindingsPreliminary results show the greatest environmental impacts for animal-based foods, particularly beef, in agreement with current literature. Foods with greater nutritional quality also generally show greater environmental sustainability, with some exceptions for particular impact categories. InterpretationThe database and index have potential to serve as powerful tools to support researchers, policy makers, and consumers, harnessing big data to drive efficient food and climate solutions for systems transformation. FundingProvince of Ontario and University of Toronto, CIHR SMART Healthy Cities Training Platform, and University of Toronto's Temerty Faculty of Medicine.

The dynamics of green innovation, environmental policy and energy structure for environmental sustainability; Evidence from AfCFTA countries

Environmental pollution caused by many factors including energy use has been a major problem around the world. A sustainable energy structure can help improve environmental sustainability. The roles of green innovation and environmental policies in determining an effective energy structure and achieving environmental sustainability have gained attention in recent research, but there are gaps to fill. Given the rapid development and unification of Africa to boost economies, this article aims to establish whether or not green innovation and environmental policies have influenced energy structure and environmental sustainability. Thirty-one (31) countries out of the 55 African Continental Free Trade Area (AfCFTA) countries were used in the analysis. The study adopted the Fully modified ordinary least squares (FMOLS) and the Dynamic ordinary least squares (DOLS) models in its analysis. Major empirical results revealed that a 1% increase in green innovations reduces ecological footprints by 0.7% and 1% (DOLS and FMOLS). Also, a percentage increase in environmental policies can reduce ecological footprints by 10.12% (DOLS) and 21.87% (FMOLS). A 1% rise in renewable energy improves their environmental sustainability by 3.26% (DOLS) and 1.82% (FMOLS). A 1% consumption of non-renewable energy reduces environmental sustainability by 4.59% (DOLS) and 1.55% (FMOLS). These results provide insights into the importance of green innovation, environmental policy, and renewable energy in improving environmental sustainability. Stringent measures and policies to boost green investment, sustainable energies, and environmental sustainability are necessary for the region.

Nexus between Life Expectancy, CO2 Emissions, Economic Development, Water, and Agriculture in Aral Sea Basin: Empirical Assessment

This study investigates how life expectancy is influenced by CO2 emissions, health spending, GDP, water usage, agricultural output, and renewable and non-renewable energy consumption within the Aral Sea basin, which is an environmentally catastrophic zone in the world. This research utilized data from the years 2002 to 2020 and employed various econometric approaches, including FMOLS, DOLS, and Driscoll–Kraay. The outcomes of the study reveal that health spending, GDP, water productivity, agriculture output, energy consumption, and human capital have a positive impact on life expectancy, but CO2 emissions have a negative impact on life expectancy. The most important policy takeaway from this study is the need to develop and implement comprehensive policies that take into account health spending, GDP, water, agricultural output, energy consumption, and education level in order to ensure life longevity.

The effects of governance quality on renewable and nonrenewable energy consumption: An explainable decision frame

AbstractThis study analyzes the effect of governance quality (six aspects: government effectiveness; control of corruption; voice and accountability; regulatory quality; political stability and absence of violence; and rule of law) on the renewable and nonrenewable energy consumption prediction based on the SHapely Additive exPlanations method for model analysis and interpretability. The empirical findings indicate that the time‐varying contributions of six aspects of governance quality on nonrenewable (renewable) energy consumption predicting vary greatly in E‐7 and G‐7 countries. The time‐varying contribution of governance quality within countries is heterogeneous and asymmetrical, especially India (Germany) in E‐7 countries (G‐7 countries). The prediction contribution distribution of governance quality between countries is more discrete in G‐7 countries than E‐7 countries. Our results are of great importance to policymakers and investors for enhancing the renewable energy consumption level in overcoming environmental challenges based on the country itself through governance quality.

Analyzing the Effects of Renewable and Non-renewable Energy Consumption on the Environment and Economic Growth: Panel Data from South Asian Countries

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The primary goal of this research is to examine how different types of energy are used in South Asian countries and how they affect the environment and economic growth. From 1990 through 2021, this research employed Panel Data from multiple sources covering the South Asian countries of Pakistan, India, Bangladesh, Sri Lanka, Nepal, Bhutan, Maldives, and Afghanistan. The results are determined by using the Fixed Effect Regression (FEM) technique in both models. Several significant findings are supported by the data. First, while using renewable energy flattens carbon dioxide effusion, using non-renewable energy sources increases it. CO2 emissions are affected negatively by gross domestic product but positively by gross domestic product square. These results provide credence to the idea that South Asian countries do experience an Environment Kuznets Curve. Furthermore, foreign direct investment has a beneficial effect on CO2 emissions, lending credence to the Pollution Haven Hypothesis. Model 2 instead finds that both renewable and non-renewable energy consumption contributes to economic expansion. Gross Fixed Capital Formation and foreign direct investment also contribute to economic growth in this area. Finally, Inflation retards economic expansion.</span></p>

The Effect of Energy Security on Economic Growth in ASEAN During 2000–2020

Energy is an important input for economic growth. The increase in non-renewable energy consumption has an impact on energy insecurity. This study aims to calculate energy security and analyze its effect on ASEAN economic growth during the 2000–2020 period. The method used in this study is the Principal Component Analysis to calculate energy security and the Feasible Generalized Least Square panel data regression to analyze its effect on economic growth. The authors use four dimensions to build an energy security index, namely availability, accessibility, acceptability, and efficiency. Several variables are included in the model, are capital, labor, trade, and world oil price. The results of this study indicate that the index and dimensions of energy security have a positive influence on ASEAN economic growth, except for the efficiency dimension. Capital. employment, trade, and oil prices have a positive influence on ASEAN economic growth. Regional Governments need to reduce the gap in the electrification ratio in several countries and build energy infrastructure. The government also needs to increase the application of energy diversification, increase renewable energy production and the need to pay more attention to environmental policies in economic activities.

Sustainable Development in Indonesia: A Study of Energy Consumption, CO2 Emissions, FDI, and Gross Capital Formation

The purpose of this study is to examine how the consumption of renewable and nonrenewable energy, CO2 emissions, FDI, and gross capital formation affect the GDP per capita in the case of Indonesia. This study employs cointegration and ARDL to estimate the short and long-run coefficient which is preceded by ADF and PP unit root test using the annual time series data from 1960 to 2021. The result of the estimation shows that in the long run non-renewable energy consumption, CO2 emission, and FDI impact the economic growth of Indonesia directly. Meanwhile, in the short run, the estimation result reveals that non-renewable energy and FDI are positive and statistically significantly affected the economic growth of Indonesia. Renewable energy hasn't yet been found to have a substantial impact on Indonesia's economic growth. This research offers novel perspectives on how nonrenewable and renewable energy consumption, CO2 emissions, and FDI impact the economic growth. The findings provide valuable implications for Indonesia to develop long-term policies that can enhance the positive effects of energy consumption and CO2 emissions on economic growth in the future. The involvement of FDI in the model also become the novelty of this study to examine the impact of FDI to economic growth.

Preparation of carbon/Fe-doped g-C3N4 and study on its photocatalytic hydrogen production performance

With the large consumption of non-renewable energy, energy shortage has become a major challenge to human society. Among many new energy production technologies, photocatalytic hydrogen production from water can be realized by using abundant solar energy as the driving force, and the conditions of hydrogen production are mild, green and pollution-free, it is considered one of the effective technologies to solve the current energy shortage crisis. The core of photocatalytic hydrogen production is photocatalyst, therefore, the study of efficient, stable and cheap photocatalysts has become a hot spot. Two-dimensional layered g-C3N4 has become an important photocatalytic material in recent years because of its suitable band gap, its response in the visible light band and its chemical and thermal stability. Due to the high rate of photogenerated electron-hole recombination and the slow rate of photoreaction of g-C3N4, the addition of [Fe2(HMOPIP)6]·8H2O·10OH to regulate g-C3N4 forming carbon/Fe-doped g-C3N4, it can reduces the recombination efficiency of photogenerated electron-hole and improves photocatalytic activity. Through hydrogen production experiment, we can see that, compared with g-C3N4, the carbon/Fe-doped g-C3N4 has better electron-hole separation ability and photocatalytic hydrogen production performance, with the hydrogen production rate of the carbon/Fe-doped g-C3N4 being approximately 5.92-fold higher than g-C3N4.

The impact of financial development and energy consumption on ecological footprint in economic complexity‐based EKC framework: New evidence from BRICS‐T region

AbstractThis study investigates the impact of financial development, nonrenewable energy, renewable energy, and trade openness on the ecological footprint from 1990 to 2020 under the new hypothetical imitations of the economic complexity‐induced environmental Kuznets curve (EKC) framework in BRICS‐T economies. After verifying the potential cross‐sectional dependency, this study employed second‐generation panel data tests to estimate the consistent, unbiased, and robust results. The key results of this research discover that the influence of economic complexity increases pollution at the initial stage; however, at the second stage such as the square of economic complexity significantly reduces it, which indicates the confirmation of the EKC hypothesis in these economies. Moreover, financial development and nonrenewable energy consumption significantly increase the level of ecological footprint. In contrast, renewable energy consumption curtails the pollution level in all quantiles. The results provide insight for government and policymakers to diminish the ecological footprint in BRICS‐T economies through clean energy technologies and diversification, such as carbon storage and capture.

Transparent AI-assisted chemical engineering process: Machine learning modeling and multi-objective optimization for integrating process data and molecular-level reaction mechanisms

Thoroughly utilizing the first principles of chemical processes, industrial big data, and artificial intelligence algorithms has been a deterministic trend in process modeling technology. However, the complexity of reaction networks triggers austere challenges to the deeper understanding of hybrid modeling techniques. Herein, this study explores a universal framework that integrates artificial intelligence algorithms and process mechanisms. The concept of “transparent AI-assisted chemical processes” is proposed. This study meticulously explains how to build a database integrating process big data and reaction network data. Based on the established machine learning framework incorporating process big data and mechanisms, multi-objective optimization algorithms are combined to achieve process optimization. The results indicate that the comprehensive dimensions of the optimized process's technical performance, economic performance, and environmental impact have energetically upgraded. Compared to the pre-optimization process, the optimized process's conversion rate and high-value product yield have increased by 4.44% and 4.25%, respectively. Moreover, the optimized process's non-renewable energy consumption and greenhouse gas emissions decreased by 6.23% and 12.60%, respectively.

Nonparametric analysis of the relationship between income inequality and energy consumption in African countries

AbstractEnergy usage and disparities in income have emerged as the major threats that hinder the achievement of sustainable development goals in Africa. This study aims to assess the time‐varying impact of income inequality on both renewable and nonrenewable energy consumption, utilizing both parametric and nonparametric models across a panel of 18 African countries from 1990 to 2015. We find that, according to the parametric model, income inequality does not affect energy consumption. However, the nonparametric estimates point the presence of both positive and negative relationship between the two variables at different time period. The time varying impacts reveal that unfair income distribution affects renewable energy consumption via the dominance of some channels at specific times. Accordingly, the findings of this study emphasize that policymakers in African countries need to give particular heed to the role played by the income inequality in designing cleaner and greener energy transition policies.

Empirical evidence of emissions discourse related to food, beverage, and tobacco production in leading manufacturing nations.

The proliferation of carbon dioxide (CO2) emissions has resulted in significant environmental challenges worldwide, particularly within industrialized nations. The present paper tries to investigate the role of food, beverages, and tobacco in mitigating CO2 emissions in the top industrial countries. Economic growth and renewable and non-renewable energy consumption are integrated as control variable in the empirical model. The study applied the pooled mean group-autoregressive distributed lag (PMG-ARDL) model proposed by (J Appl Econ, 16:289-326, 2001) and Granger causality test for causalities directions. The empirical outcomes suggest the presence of cross-sectional dependence, and variables are integrated of order one and cointegrated. Long-run estimates revealed the presence of inversed U curve proving the validity of the environmental Kuznets curve. Also, the results show that renewable energy (RE) consumption contribute in reducing emissions, while non-renewable energy (NRE) and food, beverages, and tobacco (FBT) led to increase emissions level in the long run. Granger shows bidirectional long-run relationship between CO2 emissions, non-renewable energy, and economic growth. Moreover, Granger recorded no causality between food, beverages, and tobacco and any other variable. We recommend that firms engaged in the production of food, beverages, and tobacco products are increasingly urged to make investments in clean technologies powered by renewable energy sources for their manufacturing processes. This is considered a necessary step to achieve a significant reduction in CO2 emissions over an extended period.