Assessing the Impact of Renewable Energy Integration on Energy Efficiency within the China-Pakistan Economic Corridor (CPEC)

Assessing the Impact of Renewable Energy Integration on Energy Efficiency within the China-Pakistan Economic Corridor (CPEC) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing the Impact of Renewable Energy Integration on Energy Efficiency within the China-Pakistan Economic Corridor (CPEC) Anis Bensadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4783399/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The China-Pakistan Economic Corridor (CPEC) is a major development project that aims to enhance connectivity and cooperation between China and Pakistan. It is part of China's broader Belt and Road Initiative (BRI), which seeks to improve trade routes and economic integration across Asia, Africa, and Europe. The study examines the impact of renewable energies such as hydro, biofuel, solar PV, and geothermal on energy efficiency within the CPEC. The findings reveal significant inefficiencies in the hydroelectric and biofuel energies, where energy production increases while efficiency declines. In contrast, solar PV and geothermal power had no discernible impact on the energy efficiency, suggesting that either these technologies are not yet fully utilized, since still being in the early stages of integration into CPEC's energy grid. The study recommends implementing several practices such as modernizing infrastructure by replacing outdated facilities with advanced technology to improve energy conversion and transmission efficiency. Integrating automation and control systems to minimize losses and enhancing maintenance practices to extend facility lifespan and performance. Scaling up solar PV and geothermal technologies through supportive policies and investments. Integrating smart grid technologies to enhance renewable energy effectiveness through better energy management. Finally, increase research and development investment, with essential collaborations between academia, research, and the energy industry to develop tailored solutions to meet the unique needs of the CPEC project. Physical sciences/Energy science and technology/Energy storage Physical sciences/Energy science and technology/Fossil fuels Physical sciences/Energy science and technology/Renewable energy Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Renewable Energies Energy Efficiency China-Pakistan Economic Corridor (CPEC) Sustainable Development. Figures Figure 1 Figure 2 1. Introduction The China-Pakistan Economic Corridor (CPEC) is a flagship infrastructure development project that aims to enhance economic cooperation and connectivity between China and Pakistan. It includes a network of transportation, energy, and industrial projects spanning across Pakistan, with a focus on improving trade routes, energy infrastructure, and economic development. CPEC is considered a strategic component of China's Belt and Road Initiative (BRI), aiming to stimulate economic growth, create employment opportunities, and foster regional connectivity. The project holds immense significance for both countries, with Pakistan benefiting from infrastructural development and economic growth, and China gaining access to new markets and trade routes. (Ahmad M., 2019). Energy efficiency within the CPEC refers to the adoption of strategies, technologies, and practices aimed at reducing energy consumption while maintaining or improving the level of energy services. This involves optimizing the energy use across various sectors, such as industrial, residential, and transportation, within the scope of CPEC's infrastructure and development projects. CPEC includes industrial projects where energy efficiency measures are implemented, such as adopting high-efficiency equipment, process optimization, waste heat recovery, and advanced manufacturing technologies to reduce energy wastage (Bhatti, 2020 ). Energy efficiency involves constructing buildings with improved thermal insulation, energy-efficient windows, HVAC systems, and energy-saving lighting systems, as well as retrofitting existing structures to improve their energy performance. Transportation efficiency focuses on developing public transportation, promoting EV usage, and optimizing logistics operations to reduce fuel consumption, with CPEC projects often including EV charging stations. Smart grid technologies are deployed to optimize energy distribution and use, supported by policies and regulatory frameworks that encourage energy-efficient practices, including setting standards and offering incentives (Wang, 2018 ). The integration of renewable energy sources with energy efficiency measures maximizes benefits, reducing greenhouse gas emissions and operational costs while enhancing competitiveness. Energy efficiency initiatives in CPEC contribute to building local capacity through training programs and initiatives that rise awareness of energy-efficient technologies among stakeholders and the public, ultimately supporting the corridor's sustainable development goals. The CPEC project presents a range of challenges and opportunities in the energy sector, particularly concerning sustainable development, as the predominant reliance on traditional energy sources, primarily fossil fuels, contains environmental risks and undermines sustainability efforts (Sheikh, 2019 ). In addition, the persistent underperformance of energy infrastructure within the CPEC often results in ongoing energy shortages, hampering economic recovery efforts (Nazir, 2020 ). Furthermore, ensuring compatibility of existing infrastructure with energy-efficient technologies and retrofitting solutions may be a significant challenge in implementing energy efficiency measures. Moreover, effectively integrating renewable energy sources with energy efficiency measures requires careful planning and coordination, which may face technical, logistical, and strategic challenges. Addressing these issues through innovation and adaptive practices could establish a framework guiding China's environmental and economic policies (Li, 2019 ). Finally ensuring the long-term maintenance and sustainability of energy-efficient systems and technologies poses a challenge, as proper upkeep and ongoing support are critical for continued efficiency gains. The optimization of energy efficiency can aid China in addressing two simultaneous challenges: sustainable energy provision and economic growth at reasonable costs. The purpose of the study is to investigate the impact of renewable energies on energy efficiency within the CPEC project to develop a comprehensive understanding of how integrating renewable energy sources to enhance energy efficiency, thereby optimizing resource utilization and promoting long-term sustainability. By providing empirical evidence and actionable insights, the study aims to inform policymakers and stakeholders involved in CPEC, aiding in the design and implementation of effective policies. (Jiang, 2021) Economically, assessing the impact of renewable energies use on energy efficiency can help designing policies leading to cost savings and economic growth. Additionally, energy security can be enhanced by reducing dependency on imported fossil fuels and diversifying the energy mix. The research also aims at providing recommendations related to the use of the necessary technological innovations and advancements, environmental benefits such as reduced greenhouse gas emissions. By filling existing knowledge gaps and promoting greater stakeholder engagement, the study sets the stage for future research and provide a comprehensive analysis of current practices, challenges, and opportunities in the integration of renewable energy and energy efficiency within large infrastructure projects like CPEC. The study investigating the impact of renewable energies on energy efficiency within the CPEC project offers several significant benefits. First, enhanced energy sustainability is achieved by understanding how renewable sources can improve efficiency, optimizing resource use, reducing dependence on fossil fuels, lowering greenhouse gas emissions, and contributing to environmental conservation. Second, economic savings are realized as efficient energy use through renewables significantly reduces costs for consumers and businesses, thereby enhancing economic performance, providing operational cost savings, and stimulating regional economic growth. Third, improved energy security is highlighted by reducing reliance on imported and fossil fuels, with the study showcasing ways to boost energy security by creating a more resilient energy system through a diverse mix and optimized energy use. Fourth, the study provides valuable insights for policymakers, aiding the development and implementation of effective strategies and regulations for integrating renewable energy and energy efficiency measures in energy infrastructure projects. Fifth, technological advancements are driven by exploring the synergy between renewable energy and efficiency, leading to innovations in energy management systems and smart grid technologies. Sixth, environmental benefits are evident through reduced air pollution and carbon emissions, contributing to national and international climate goals and improving public health. Seventh, informed infrastructure planning is facilitated by insights from the study, ensuring energy infrastructure projects under CPEC are designed for energy efficiency and compatibility with renewable systems, maximizing benefits and sustainability. Eighth, social and economic equity is enhanced by identifying cost-effective strategies, making renewable energy benefits accessible to broader populations, including underserved and rural communities, thereby promoting social equity and inclusive development. Ninth, stakeholder engagement and awareness are raised, fostering involvement from businesses, governments, and the public in supporting sustainable development initiatives. Finally, the study offers academic and practical contributions by filling literature gaps, adding to the body of knowledge on energy efficiency and renewable energy integration, and providing actionable insights and best practices for similar global projects. 2. Literature Review 2.1 Overview of Renewable Energies in China China's renewable energy capacity more than tripled between 2010 and 2020 and became the world's leader in installed renewable energy capacity, particularly in solar and wind power. By 2022, China's wind and solar capacities had reached 282 GW and 306 GW, respectively, making significant contributions to the global renewable energy market (Khan M. A., 2018 ). The Chinese government has played a crucial role in promoting renewable energy through various policies and initiatives discuss the implementation of the Renewable Energy Law and the 13th Five-Year Plan, which set ambitious targets for renewable energy development. Moreover, government subsidies, feed-in tariffs, and favourable investment policies have provided substantial financial support for renewable energy projects (Zheng, 2020 ). The "14th Five-Year Plan" continues to promote clean energy transitions, emphasizing the importance of technological innovation and infrastructure development. The shift towards renewable energy has had notable economic and environmental impacts in China. Studies such as (Xu, 2018) highlight the positive effects on energy security, job creation, and economic diversification. The renewable energy sector has also contributed to a significant reduction in carbon emissions and air pollutants, improving public health and environmental quality. However, challenges such as grid integration and intermittency of renewable energy sources remain critical issues (Li, 2019 ). Technological advancements have been pivotal in improving the efficiency and cost-effectiveness of renewable energy technologies. Significant progress in photovoltaic cell efficiency, wind turbine design, and energy storage solutions (Zhao, 2022 ). Additionally, China's investments in smart grid technologies and digitalization are enhancing the integration and management of renewable energy into the national grid (Wang, 2022). Despite remarkable progress, China's renewable energy sector faces several challenges. Grid integration issues, regional disparities in resource distribution, and the need for technological innovations in energy storage are critical areas for future research and policy development (Duan, 2022 ). Additionally, balancing economic growth with environmental sustainability remains a complex issue, requiring coordinated efforts at multiple governance levels (Shah, 2021 ). 2.2 Renewable Energy Technologies in China China has emerged as the global leader in solar energy, prioritizing the advancement and deployment of photovoltaic (PV) panels while continuously enhancing efficiency and cost-effectiveness. Additionally, there have been significant advancements in wind turbine technology, resulting in the development of larger and more efficient turbines, including the installation of offshore wind farms. Consequently, the contribution of wind energy to the national grid has notably increased (Hussain, 2018 ). Hydroelectric power plays a pivotal role in China's renewable energy strategy, driving advancements in turbine technology and the implementation of integrated small-scale projects aimed at rural development. China's bioenergy technologies are also noteworthy, with progress in biomass gasification and the conversion of agricultural waste into energy to minimize waste and generate power. Research and development efforts in these areas receive substantial funding from both government and private sectors (Jiang, 2021). The Chinese government's commitment to renewable energy development is evident in its five-year plans and the implementation of the Renewable Energy Law, which provides grants and subsidies to spur innovation. Simultaneously, the private sector, driven by profit maximization motives and governmental tax incentives, heavily invests in the advancement of new technologies. This collaborative effort between the public and private sectors highlights a synergistic approach to promoting renewable energy technology. 2.3 China’s Policies on Renewable Energy and CPEC China has implemented regulatory and institutional mechanisms for renewable energy development within the CPEC, employing tailored measures to promote its advancement. These measures include the enactment of the Renewable Energy Law and the implementation of the 13th Five-Year Plan, specifically designed to foster the development of renewable energy sources such as solar, wind, hydro, and biomass (Cevik, 2024 ). Notably, these regulations mandate utilities to purchase 100% of the renewable power they produce, reflecting the confidence of the energy market in supporting green energy initiatives. The CPEC's foundation rests upon a national legislative framework and the establishment of bilateral treaties that ensure the creation of renewable energy infrastructure. Projects like the Pakistan UEP Wind Farm and Dawood Wind Farm exemplify the outcomes of this policy, funded, and constructed with the assistance of Chinese expertise and capital. These initiatives not only contribute to the comprehensive realization of China's environmental objectives, such as emission reduction and power source diversification with increased reliance on non-fossil fuels, but also align with China's economic goals of ensuring secure and sustainable energy supplies for its rapidly expanding industries while promoting the export of renewable energy technologies (Creutzig, 2018 ). In addition to the policies, the Belt and Road Initiative (BRI) represents a multilateral development strategy aimed at enhancing regional connectivity and integration for economic purposes. The synergy between China's domestic economic endeavours and its international economic policies is underscored by this initiative. 2.4 Importance of Energy Efficiency Energy efficiency is vital for minimizing energy waste, reducing greenhouse gas emissions, and lowering energy costs. Energy efficiency improvements could account for nearly half of the reductions required to meet global climate targets. Enhanced energy efficiency can also alleviate pressure on energy supply systems and contribute to sustainable development (Duan, 2022 ). Technological advancements have played a crucial role in improving energy efficiency across various sectors, including residential, industrial, and transportation. The development of smart grids, which optimize energy distribution and consumption through advanced monitoring and control systems. Additionally, the advent of high-efficiency appliances and industrial processes has led to significant energy savings (Worrell, 2020 ). Innovations in building materials and design are leading to substantial energy savings. For instance, passive technologies and zero-energy buildings are becoming increasingly feasible due to advancements in insulation, and HVAC systems (Wei Y. e., 2021). The integration of Internet of Things devices in building automation systems allows for real-time energy management and further efficiency gains (Tang, 2021 ). Effective policy measures and regulatory frameworks are essential for promoting energy efficiency. Policies that provide financial incentives, such as tax credits, rebates, and grants, have been shown to accelerate the adoption of energy-efficient technologies (Shan, 2021 ). Mandatory energy performance standards and labelling programs help consumers make informed decisions and stimulate market demand for high-efficiency products (Rizvi, 2019 ). Improving energy efficiency has both economic and environmental benefits. Investments in energy efficiency can result in significant cost savings for businesses and consumers. These savings often outweigh the initial investment costs, making energy efficiency financially attractive (O’Dwyer, 2019 ). Environmentally, enhanced energy efficiency contributes to significant reductions in greenhouse gas emissions and air pollutants. Energy efficiency improvements in industries have led to substantial emissions reductions, aiding in the fight against climate change. In the industrial sector, (Worrell, 2020 ) provide evidence that energy efficiency measures, such as process optimization and the adoption of advanced manufacturing technologies, can drastically reduce energy consumption and costs. The transportation sector has also seen improvements, with electric and hybrid vehicles playing a key role in enhancing fuel efficiency (Longhurst, 2019 ). Despite significant progress, numerous challenges remain. Energy efficiency improvements often face barriers such as high upfront costs, insufficient financing options, and a lack of awareness or information (Lacey-Barnacle, 2020 ). Overcoming these barriers requires comprehensive strategies that include robust policy support, stakeholder engagement, and continuous technological innovation. 2.5 Comparative Studies The comparative analysis conducted has provided valuable insights into how the energy efficiency practices within the CPEC, under China's framework, measure up against international examples and regional practices within China itself (Irshad, 2019 ). China's preference for centralized approaches has facilitated swift implementation and scaling up of renewable energy projects. A regional examination within China reveals variations in the adoption and energy efficiency policies. Coastal regions like Jiangsu and Guangdong, boasting robust industrial bases and higher economic outputs, have witnessed rapid adoption of green technology and substantial investments in renewable energy projects, unlike inland regions progressing at a different pace. Such disparities underscore the complexity of policy implementation and present both challenges and opportunities for policymakers to navigate diverse economic landscapes across the country. On a global scale, China's commitment to CPEC demonstrates how energy efficiency management can accelerate the transition to renewable energy in developing countries. This approach contrasts sharply with the U.S. model, which leans more heavily on private investment, highlighting the diverse applications of energy management worldwide (O’Dwyer, 2019 ). 2.6 Gaps in Existing Literature The existing literature on energy efficiency initiatives within the CPEC often underscores the immense potential for sustainable development brought by this extensive infrastructure project. Studies addressing energy efficiency within CPEC initiatives, often focusing on the potential of energy savings in industrial and residential sectors (Khan M. S., 2020 ). However, a notable gap in the literature is the limited to the examination of specific energy efficiency measures tailored for CPEC’s infrastructure projects. Furthermore, the relationship between energy efficiency improvements and the performance of renewable energies under CPEC is not well-established. This research addresses this gap by employing a quantitative approach by investigating the impact of renewable energies on energy efficiency with the CPEC project, which has not been utilized in prior studies. The approach for this study incorporates unique aspects that are specifically applicable to the CPEC project, offering new insights. The study investigating the impact of renewable energies on energy efficiency within CPEC can offer comprehensive benefits across economic, environmental, social, and technological dimensions, ultimately contributing to more sustainable and resilient energy systems. 3. Methodology 3.1 Research Design The methodology employed in this research adopts a quantitative approach to conduct a comprehensive examination of the effects of renewable energies on energy efficiency within the framework of the CPEC. Due to the nature of the study, quantitative approach is chosen for its ability to produce objective, reliable, and generalizable results, as it is particularly effective when the research aims to measure variables, investigate statistical correlations between different variables, support robust analysis and informed decision-making. The quantitative analysis entails the collection and examination of numerical data pertaining related to energy efficiency (Olabi, 2022 ). The objectives of this study can be achieved by applying a regression model and a correlation analysis to analyze these datasets, thereby identifying predominant patterns, and establishing connections between energy efficiency, and renewable energies employed within the CPEC such as hydro, biofuel, solar PV, and geothermal. Through this analysis, the study aims to evaluate impact renewable energies on energy efficiency. 3.2 Data Collection The data used to achieve the purpose of this study has been collected from the following sources: Kaggle : Provides extensive range of datasets on renewable energy inputs and outputs, including crucial indicators such as investment figures, innovations, and quantitative results like energy generated and conserved. Global Data Repository on Renewable Energy : This source provides records of renewable energy projects worldwide, including project funding and efficiency ratings, which can be compared with those of CPEC. CPEC Official Database : Established by the involved governments, this database offers information on the size, investment amounts, and technology types of CPEC’s energy projects. International Renewable Energy Agency (IRENA) Database : IRENA provides comprehensive data on renewable energy generation, government policies, and financial support for integrating renewable energy into the energy mix, supplementing the analysis of CPEC projects. The dataset chosen encompasses relevant and effective data pertinent to the study's scope. Key factors explored within the dataset include levels of investment in green technologies, exploration of innovative solutions in renewable energy, and measurable outcomes such as energy production, efficiency, and sustainability (Nazir, 2020 ). Additionally, the dataset incorporates details related to the primary country under examination, specifically focusing on CPEC project specifics within the defined timeframe. 3.3 Mathematical Model Dependent Variable Energy Efficiency Ratio Energy output/input, also known as the energy efficiency ratio or coefficient, is a measure of how effectively a system converts input energy into useful output energy (Al-Shetwi, 2022 ). It is calculated as the ratio of energy output to energy input. It is determined by the amount of energy produced by the system relative to the amount of energy used by the system. EER (Energy Efficiency Ratio) is calculated using the following formula $$\:\text{E}\text{n}\text{e}\text{r}\text{g}\text{y}\:\text{E}\text{f}\text{f}\text{i}\text{c}\text{i}\text{e}\text{n}\text{c}\text{y}=\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{R}\text{e}\text{n}\text{e}\text{w}\text{a}\text{b}\text{l}\text{e}\:\text{E}\text{n}\text{e}\text{r}\text{g}\text{y}\:\text{O}\text{u}\text{t}\text{p}\text{u}\text{t}\:\left(\text{T}\text{W}\text{h}\right)\text{}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{R}\text{e}\text{n}\text{e}\text{w}\text{a}\text{b}\text{l}\text{e}\:\text{E}\text{n}\text{e}\text{r}\text{g}\text{y}\:\text{I}\text{n}\text{p}\text{u}\text{t}\:\left(\text{T}\text{W}\text{h}\right)}$$ A higher energy efficiency ratio indicates that a system has effectively converted a larger proportion of the input energy into useful output energy, reflecting a more efficient operation. On the other hand, a lower energy efficiency ratio signifies that a system has wasted more input energy and produced less useful output energy, indicating lower efficiency. Energy output/input is a crucial metric used in various industries and systems to evaluate the performance and efficiency of energy conversion processes, appliances, equipment, and overall energy systems. Increasing energy efficiency is essential for cost savings, environmental sustainability, and overall system optimization. Independent Variables Hydro (TWh) This variable quantifies electricity from hydroelectric power that is generated by harnessing the energy of flowing or falling water. This power is generated by converting the kinetic energy of flowing water into electrical energy using turbines. Biofuel (TWh) This variable quantifies biofuel derived from biological materials, often referred to as biomass. These materials can include plant matter, animal waste, and other organic substances. Biofuels are used as alternatives to fossil fuels in various applications, such as transportation, heating, and electricity generation and are considered as renewable energies. Solar PV (TWh) This variable quantifies the electricity generated from Solar PV (Photovoltaic) technology that converts sunlight directly into electricity using semiconductor material. Geothermal (TWh) This variable quantifies the energy source that harnesses the heat stored beneath the Earth's surface to generate electricity or provide heating and cooling for buildings. Mathematical equation The regression model is designed to assess the relationship between "Energy Efficiency" (dependent variable) and energy production from various renewable sources (independent variables): Energy Efficiency Ratio = \(\:\beta\:0\:+\:\beta\:1\:\left(Hydro\:TWh\right)\:+\:\beta\:2\:\left(Biofuel\:TWh\right)\:+\:\beta\:3\:\left(Solar\:PV\:TWh\right)\:+\:\beta\:4\:\left(Geothermal\:TWh\right)\:+\:\epsilon\:\) β₀ (Intercept) : Represents the baseline level of energy efficiency when all independent variables are zero. β₁, β₂, β₃, β₄ (Coefficients) : Measure the changes in energy efficiency associated with each one-unit change in the independent variables. ε (Error Term) : This term captures variations in energy efficiency that the independent variables cannot explain. Whereas E( ε ) = 0 This model seeks to quantify the impact of various renewable energy sources on energy utilization efficiency. It aims to examine the impact of renewable energies such as hydro, biofuel, solar PV, and geothermal on energy efficiency within the CPEC. 3.4 Descriptive Statistics The Table 1 below represents the descriptive statistics of the dependent variable and independent variables during a timeframe from 2000 to 2022. Table 1 Descriptive Statistics of the dataset 2000–2022 Hydro (TWh) Biofuel (TWh) Solar PV (TWh) Geothermal (TWh) Energy Efficiency Valid 300 300 300 300 300 Missing 0 0 0 0 0 Mean 1190.222 294.018 79.060 0.125 1.166 Std. Deviation 70.515 17.307 4.426 0.007 0.086 Minimum 1070.856 265.872 71.522 0.113 0.986 Maximum 1307.892 324.362 87.296 0.137 1.395 Hydro (TWh ) Hydroelectric power demonstrates a relatively high mean generation level of 1190.22, accompanied by a standard deviation of 70.51. The range spans from the lowest value of 1070.85 to the highest value of 1307.89 indicates that the production of power is not stable, these fluctuations can be attributed to various factors such as annual rainfall and water availability (Khan et al., 2018), underscoring their potential influence on hydroelectric output. Biofuel (TWh ) Biofuel production exhibits a mean at 294.01 with a standard deviation of 17.30 indicating less variability. The range of wind farm power generation capacity, spanning from 265.87 TWh to 324.36 TWh, suggests sustained production at a high level, likely attributable to policy support and the abundant availability of biomass resources. Solar PV (TWh) The solar PV mean production is considerably lower at 79.06. The standard deviation is small at 4.42, meaning a rather stable trend in solar PV deployment, possibly due to technological progress and cost decline. Geothermal (TWh) Geothermal energy, conversely, exhibits the smallest mean production at 0.12 TWh, coupled with an exceedingly low standard deviation of 0.007, indicating highly stable production levels that remain close to what is expected. This suggests that geothermal energy plays a marginal role in China's renewable energy agenda, likely influenced by geographical constraints. Energy Efficiency The mean energy efficiency stands at 1.16, with a standard deviation of 0.08. This metric signifies the efficiency of energy conversion from production to usable energy, ranging from 0.98 to 1.39, indicating variations in efficiency among sectors or regions. The range implies existing mechanisms for optimizing energy efficiency yet underscores the need for further improvement in maximizing energy efficiency. The descriptive statistics provide a groundwork for understanding the scales and distributions of renewable energy sources in China (Bhatti, 2020 ). The relatively stable production levels observed across most energy types, apart from hydro, which exhibits some variability, suggest a consistent development trajectory within China's renewable energy sector amidst relatively unchanged policy environments. This observation serves as a prelude to further investigation into the interactions between these energy sources and economic variables, as well as their impact on energy efficiency. 3.5 Regression Analysis The research objective is to investigate the impact of renewable energies such as Hydro, Biofuel, Solar PV, and Geothermal, on energy efficiency, using CPEC data from 2000 to 2022. Linear regression analysis relies on several key assumptions that are essential for ensuring the validity and reliability of its results. Understanding and verifying these assumptions for accurate model interpretation and prediction is essential. These assumptions are as follows: Linear Relationship : The fundamental premise of multiple linear regression is that there is a linear relationship between the dependent (outcome) variable and the independent variables. This linearity can be visually assessed using scatterplots in the Appendix displaying a straight-line relationship rather than a curvilinear one. Multivariate Normality : The analysis assumes that the residuals (the differences between observed and predicted values) are normally distributed. This assumption is evaluated by examining histograms or Q-Q plots of the residuals included in the Appendix . No Multicollinearity : It is crucial that the independent variables are not excessively correlated with each other, a condition known as multicollinearity. This can be assessed using correlation matrices in the Appendix . This analysis below takes all the assumptions above to answers the research objective and presents the findings from the regression models for the period 2000–2022. Regression Results The first regression model analyzes how hydro, biofuel, solar PV, and geothermal energy production affect energy efficiency. Here below are the results from the regression analysis shown in Table 2 as follow: Table 2 Regression Analysis Variable Coefficients Standard Error Standardized t p Hydro (TWh) -7.642×10 − 4 5.320×10 − 5 -0.628 -14.364 < .001 Biofuel (TWh) -9.580×10 − 4 2.165×10 − 4 -0.193 -4.424 < .001 Solar PV (TWh) -5.235×10 − 4 8.496×10 − 4 -0.027 -0.616 0.538 Geothermal (TWh) -0.655 0.526 -0.055 -1.244 0.214 Hydro (TWh) The coefficient is -7.642×10 − 4 (p < 0.01), indicating a highly significant negative impact on energy efficiency. This suggests that an increase in hydroelectric power production is associated with a decrease in energy efficiency, possibly due to losses in transmission and storage. Biofuel (TWh) The coefficient is -9.580×10 − 4 (p < 0.01), indicating a significant and negative effect on energy efficiency. This suggests that biofuel production adversely impacts energy efficiency. This could be attributed to the inherently energy-intensive nature of biofuel production, which may render it less efficient compared to other energy sources. Solar PV (TWh) The coefficient for Solar is -5.235×10 − 4 (p = 0.538), indicating it is not statistically significant. This suggests that increases in energy produced from solar PV do not significantly affect energy efficiency. Thus, at its current scale of implementation, solar PV appears to have a neutral impact on efficiency. Geothermal (TWh) The coefficient for Geothermal is -0.6550663 (p = 0.214), indicating it is not statistically significant. This lack of significant impact could be due to the relatively small scale of geothermal energy production compared to other energy sources. The results indicate that hydroelectric and biofuel productions have a significant negative correlation with energy efficiency. In contrast, solar PV and geothermal production have an insignificant relationship with energy efficiency. These findings underscore the importance of evaluating renewable energy sources as they are scaled up and integrated into the energy grid. 3.6 Correlation Analysis The correlation matrix gives useful information on the interconnectivity of the main variables in the dataset, as presented on Table 3 below: Table 3 Correlation Analysis Variables Pearson's r p Hydro (TWh) - Biofuel (TWh) 0.041 0.483 Hydro (TWh) - Solar PV (TWh) 0.017 0.774 Hydro (TWh) - Geothermal (TWh) -0.079 0.171 Hydro (TWh) - Energy Efficiency -0.632 *** < .001 Biofuel (TWh) - Solar PV (TWh) -0.020 0.728 Biofuel (TWh) - Geothermal (TWh) 0.063 0.274 Biofuel (TWh) - Energy Efficiency -0.222 *** < .001 Solar PV (TWh) - Geothermal (TWh) -0.115 * 0.047 Solar PV (TWh) - Energy Efficiency -0.027 0.638 Geothermal (TWh) - Energy Efficiency -0.014 0.806 * p < .05, ** p < .01, *** p < .001 Hydro (TWh) and Energy Efficiency (-0.632, p < 0.0001) The negative correlation observed between the volume of hydroelectric power produced and energy efficiency suggests a significant relationship. This may indicate that higher hydroelectric power generation tends to coincide with lower efficiency in energy utilization. Such a correlation could be attributed to the scale of hydro projects, which might result in considerable energy loss during transmission or inefficiencies in conversion technologies. Biofuel (TWh) and Energy Efficiency (-0.222, p < 0.0001) The correlation between biofuel production and energy efficiency is moderately negative yet statistically significant. This suggests that higher levels of biofuel energy production moderately diminish energy efficiency. This trend could be ascribed to the less efficient conversion of biofuels into usable energy compared to other renewable sources, or perhaps to the energy expended in the production process of biofuels themselves. Solar PV (TWh) and Energy Efficiency (-0.027, p = 0.638) The correlation appears to be very weak and lacks statistical significance, suggesting that fluctuations in solar PV energy production have minimal to no effect on energy efficiency at the national level. This observation may imply that while solar PV technology contributes to reducing dependence on fossil fuels, its impact on the overall efficiency of energy is negligible. Geothermal (TWh) and Energy Efficiency (-0.014, p = 0.8059) Similarly to solar PV, the correlation between geothermal energy production and energy efficiency is very weak and lacks statistical significance, suggesting a negligible impact. This might be attributed to the comparatively smaller scale of geothermal energy production in relation to other energy sources. Solar PV TWh and Geothermal TWh (-0.114, p = 0.0475) This moderate negative correlation suggests that regions or periods with higher solar PV production might experience slightly lower geothermal production, or vice versa. This can be interpreted as a reflection of resource allocation preferences within renewable energy portfolios, wherein investments may shift from one source to another based on economic or environmental considerations. The correlation analysis demonstrates that the most prominent negative correlations in energy efficiency are associated with hydroelectric and biofuel productions. This insight highlights specific areas where policy interventions could be directed, such as enhancing the transmission efficiency of hydroelectric power or refining biofuel conversion technologies. The relatively minor impact of solar and geothermal sources may stem from their current limited scale or integration inefficiencies, suggesting potential avenues for further development and research. 3.6 Residual Analysis Residual analysis is conducted at the end of the study to validate the assumptions underlying the linear regression model. Histograms of residuals and scatter plots of residuals against the independent variables and fitted values are examined. The fitted values are visualized graphically to check for normal distribution of the residuals and to identify any issues related to heteroscedasticity and outliers. Histogram of Residuals The histogram of residuals is presented on Fig. 1 below: The histogram of residuals, overlaid with a normal curve, is utilized to assess the normal distribution of residuals, a fundamental assumption in linear regression analysis. The histogram displays a distribution of residuals that is approximately symmetrically centered around zero. The superimposed normal distribution curve indicates that the residuals closely approximate a normal distribution, although minor deviations are observed. Some bins exhibit slight overcrowding, notably around − 0.1 and 0. While not as pronounced as in film, these deviations are still noticeable. Overall, the shape and spread of the histogram reasonably meet the assumption of normal distribution. Therefore, statistical conclusions drawn from the regression analysis are expected to be reasonably accurate under the assumption of normality. Plot of Residuals vs. Fitted Values Plotting residuals against fitted values is presented on Fig. 2 below: Plotting residuals against fitted values provides an additional method to examine for constant variance (homoscedasticity) across all levels of fitted values, a crucial assumption for linear regression. The scatter plot displays randomly distributed residuals along the horizontal axis, indicating no discernible pattern. This absence of systematic errors is a positive indicator. Moreover, the plot does not reveal any discernible trend or curve, suggesting that the model adequately captures nonlinearities. However, there is a slight increase in residual variance for larger fitted values, possibly indicating heteroscedasticity. This observation suggests that the variance of residuals may not be consistent across all levels of fitted values. The approximately normal distribution of residuals strengthens the veracity of the model by providing the basis for the standard tests of coefficients, which are based on the normality assumption. This suggests that t-tests and F-tests can be used in regression analysis as they are suitable. The relatively larger size of residuals at higher fitted values suggests that the data may be heteroscedastic. This, in turn, may lead to the standard errors of the regression coefficient being less reliable, which in turn may lead to the hypothesis tests and confidence intervals being less reliable. One potential solution is the use of robust standard errors to correct for inference procedures in the presence of heteroscedasticity thus ensuring that the statistical conclusions are more reliable. The model provides a way of testing for the homoscedasticity assumption, and the possibility of heteroscedasticity should be subjected to further research, for instance, using tests created for this purpose or by applying corrective measures such as heteroscedasticity-consistent standard error estimators. 4. Discussion of Findings The study's findings offer valuable insights into the impact of renewable energies such as hydro, biofuel, solar PV, and geothermal on energy efficiency within the CPEC. The significant negative correlations observed between hydro and biofuel energy production and energy efficiency suggest that expansion in these sectors does not necessarily lead to more efficient energy use. This could be attributed to inefficiencies in conversion systems or losses during energy transmission. Hydroelectric projects, due to their large scale, generate substantial energy but may face challenges in effective energy distribution and utilization. Similarly, the biofuel industry, while transformative, may require enhancements in energy-intensive processes involved in converting biological materials into usable fuel. In contrast, the insignificant results regarding solar PV and geothermal energy indicate that these technologies are still in the early stages of adoption. Further scaling up is needed to discern their impact on energy efficiency. This presents an opportunity for future technology implementation and development to enhance their contributions to the energy mix. The study's findings align with existing literature, which often underscores the complexities of balancing energy production improvements with efficiency. Previous research has highlighted instances where technological advancements outpaced production, leading to energy wastage and inefficiencies, such as transmission losses in hydroelectric power and low conversion efficiency in biofuels (Longhurst, 2019 ). The findings challenge certain literature suggesting that newer technologies like solar PV and geothermal should immediately decrease energy consumption due to their lower marginal costs and reduced environmental impacts (Khan M. A., 2018 ). This discrepancy can be attributed to the nascent stage of technology adoption within the CPEC framework, which has yet to yield significant improvements in infrastructure and integration. These findings highlight the importance of a dual focus in renewable energy policy: not only increasing production but also reducing energy consumption. This approach is essential for maximizing the benefits of renewable energy investment, particularly in the realm of energy management, where the objectives encompass both environmental sustainability and economic prosperity. The insights gleaned from the CPEC extend beyond its specific context and can inform renewable energy initiatives in other regions and countries. The findings underscore the need for future funding in technological development, particularly in energy efficiency and waste reduction, which are pivotal aspects of global change mitigation. The study illustrates the intricate interplay among renewable energy generation, technological innovation, and energy efficiency. While the development of renewable energy capacities remains crucial, the conclusions suggest an equal emphasis on enhancing energy efficiency. The study advocates for further research and policy adjustments to address the identified limitations and unlock the full potential of renewable energy technologies. 5. Conclusion This research study serves as a foundational exploration into the impact of renewable energies that are on energy efficiency. Through comprehensive statistical analysis, the study identifies key weaknesses in emerging renewable energies, offering valuable insights for policymakers and stakeholders. It reveals that hydropower and biofuels, exhibit low efficiency, potentially conflicting with sustainability goals. Conversely, solar PV and geothermal energies, show limited efficiency impacts, suggesting underutilization or integration challenges. These findings highlight the need for policymakers to carefully balance renewable energy development with energy efficiency considerations. The study underscores the importance of energy efficiency, advocating for investments not only in expanding renewable energy capacity but also in enhancing technological efficiencies. Technological innovation emerges as a critical driver for bridging existing efficiency gaps in renewable energy systems. It emphasizes the necessity of continued capital investment alongside a dual focus on adopting new technologies and improving efficiency. These strategic investments are pivotal for long-term energy system development, contributing to energy security and environmental sustainability. The insights provided aim to inform ongoing enhancements to CPEC strategies, ensuring that renewable energy remains a cornerstone of sustainable development. 6. Recommendations The study concludes by offering strategic recommendations to address inefficiencies in the Chinese renewable energy sector within the framework of the CPEC. These guidelines aim to optimize existing strategies, introduce new technologies, and enhance research and development efforts to collectively elevate energy efficiency levels. The inefficiencies observed in the hydroelectric and biofuel sectors are significant and require immediate attention. The primary strategy involves modernizing infrastructure by replacing outdated facilities with new ones equipped with advanced technology aimed at improving energy conversion and transmission efficiency. Additionally, integrating advanced automation and control systems should enhance processes and minimize losses. Improved maintenance practices, can significantly extend the lifespan and enhance the performance of facilities, thereby increasing energy output and reducing costs. For technologies such as solar PV and geothermal, which currently have minimal impact on efficiency, scaling up implementation is crucial. This can be achieved by establishing appropriate policies and taxation schemes to attract both private and public investments. Integrating smart grid technologies would significantly enhance the effectiveness of the renewable sources in question by improving energy distribution management and integrating diverse energy sources. The development of energy efficiency is an ongoing process that requires continual improvement through increased investment in research and development. This investment should focus on creating more effective energy-saving technologies and enhancing existing ones. Partnerships between academia, research institutions, and the energy industry shall be pivotal in developing customized solutions to meet the unique needs of the renewable energy sector. It is, therefore, essential to collaborate with policymakers, industry leaders, and other stakeholders to ensure that China's renewable energy efforts align with national energy needs and global sustainability goals. 7. Limitations of the Study This study aims to conduct a comprehensive analysis of renewable energy efficiency within the CPEC. However, it acknowledges few limitations that may affect its results. A primary concern is the completeness and accuracy of the data. For instance, the dataset may focus on specific types of energy production or provide only general time data. While detailed descriptions of specific technologies and energy efficiency may be available, the dataset may not capture the complex effects of different technologies on energy efficiency. This could result in a skewed understanding of the energy sector and lead to incorrect decisions regarding the utilization of energy sources. Another limitation is the modeling of econometric model used in the analysis. This model is designed to analyze the relationship between energy sources and energy efficiency but may not account for the impacts of other factors such as external economic conditions, policy changes, or environmental factors are often excluded from the models, potentially limiting their ability to accurately represent real-world energy systems. Despite these limitations, the study still provides valuable insights into renewable energy production within CPEC. Future research should aim to address these limitations by incorporating more comprehensive data and employing advanced modeling methods to better capture the complexity of renewable energy systems. Declarations Author Contribution The whole study was generated, conducted by the author Anis Bensadi. Data Availability Data would be made available by request from the author Anis Bensadi. References Abid, M. &. (2018). CPEC: Benefits for Pakistan, challenges ahead. Asian Journal of International Studies , 16(2), 131-151. Ahmad, S. (2019). Energy efficiency in buildings: A case study of Pakistan. Energy and Buildings. Journal Energy and Buildings , 196, 30-39. Al-Shetwi, A. Q. (2022). Sustainable development of renewable energy integrated power sector: Trends, environmental impacts, and recent challenges. Science of The Total Environment , 822, 153645. Bhatti, H. M. (2020). Renewable energy deployment under CPEC in Pakistan: Energy security or environmental concern? Renewable and Sustainable Energy Reviews , 123, 109777. Chen, G. Q., et al. (2020). Environmental benefits of renewable energy in China: A lifecycle perspective. Journal of Cleaner Production , 263, 121280. Chen, G. Q. (2019). Environmental benefits of energy efficiency improvements in China: A lifecycle perspective. Journal of Cleaner Production , 239, 117993. Cevik, S. (2024). Climate change and energy security: the dilemma or opportunity of the century? Environmental Economics and Policy Studies , 1-20. Cherp, A. V. (2018). Integrating techno-economic, socio-technical and political perspectives on national energy transitions: A meta-theoretical framework. Energy Research & Social Science , 37, 175-190. Creutzig, F. R. (2018). Towards demand-side solutions for mitigating climate change. Nature Climate Change , 8(4), 260-263. Duan, W. K. (2022). Pakistan's energy sector—from a power outage to sustainable supply. Examining the role of China–Pakistan economic corridor. Energy & Environment , 33(8), 1636-1662. Hussain, M. e. (2018). Environmental impacts of renewable energy projects under CPEC. Environmental Science and Pollution Research , 25(21), 20435-20449. Irshad, M. S. (2019). The potential for economic growth through CPEC’s energy projects. Economic Research , 32(1), 203-219. Jiang, P. e. Extended water-energy nexus contribution to environmentally related sustainable development goals. Renewable and Sustainable Energy Reviews. 145, 111129 Kabeyi, M. J. (2022). Sustainable energy transition for renewable and low carbon grid electricity generation and supply. Frontiers in Energy Research , 09. Khan, M. A. (2018). Analysis of power plants in China Pakistan economic corridor (CPEC): An application of analytic network process (ANP). Journal of Renewable and Sustainable Energy , 10(6). Khan, M. I. (2020). An overview of global renewable energy trends and current practices in Pakistan—A perspective of policy implications. Journal of Renewable and Sustainable Energy , 12(5). Khan, M. S. (2020). Energy efficiency policies in the context of CPEC: Status and opportunities. Energy Policy , 142, 111551. Lacey-Barnacle, M. R. (2020). Energy justice in the developing world: a review of theoretical frameworks, key research themes and policy implications. Energy for Sustainable Development , 55, 122-138. Li, X. e. (2019). Renewable energy policies and barriers in the context of CPEC. Energy Policy , 128, 120-129. Longhurst, N. &. (2019). Mapping diverse visions of energy transitions: co-producing sociotechnical imaginaries. Sustainability Science , 14(4), 973-990. Nazir, A. e. (2020). Assessing the environmental footprint of CPEC’s energy projects. Environmental Monitoring and Assessment , 192(3), 246. O’Dwyer, E. P. (2019). Smart energy systems for sustainable smart cities: Current developments, trends and future directions. Applied energy , 237, 581-597. Olabi, A. G. (2022). Renewable energy and climate change. Renewable and Sustainable Energy Reviews , 158, 112111. Rizvi, S. e. (2019). Technological advancements in renewable energy solutions under CPEC. Energy Reports , 5, 761-771. Shah, S. Z. (2021). Top managers' attributes, innovation, and the participation in China–Pakistan Economic Corridor: A study of energy sector small and medium‐sized enterprises. Managerial and Decision Economics, , 42(2), 385-406. Shan, S. G. (2021). Role of green technology innovation and renewable energy in carbon neutrality: A sustainable investigation from Turkey. Journal of Environmental Management , 294, 113004. Sheikh, S. M. (2019). CPEC Investment Opportunities and Challenges in Pakistan. Journal of Accounting and Finance in Emerging Economies , 5(1), 123-128. Tang, B. e. (2021). Electric vehicle infrastructure under CPEC: Development and challenges. Journal of Cleaner Production , 282, 125437. Wei, Y. e. (2021). Overcoming barriers to energy efficiency in China: Policy recommendations and industry perspectives. Renewable Energy , 178, 108-115. Wang, Y., et al. (2022). Smart grids: Enhancing the integration of renewable energy in China. IEEE Access , 10, 45623-45633. Wang, Q. e. (2018). Improving energy efficiency in China's industrial sector: Current practices and future challenges. Applied Energy , 225, 685-694. Wei, Y. e. (2021). Policy and regulatory barriers to energy efficiency in Pakistan: Contextual analysis under CPEC. Energy Reports , 7, 117-124. Worrell, E. e. (2020). Industrial energy efficiency: Technologies and measures for significant savings. Energy Efficiency , 13(6), 1037-1056. Xu, Y., et al. (2018). Economic impacts of renewable energy in China. Applied Energy , 231, 56-67. Zhao, X. e. (2022). Smart grids and energy efficiency under CPEC: Challenges and opportunities. Renewable and Sustainable Energy Reviews , 152, 111759. Zheng, J. e. (2020). Economic benefits of energy efficiency in China: Evidence from industrial and residential sectors. Energy Economics , 86, 104696. Footnotes Data would be made available by request from the author Anis Bensadi Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Oct, 2024 Reviews received at journal 07 Oct, 2024 Reviewers agreed at journal 17 Sep, 2024 Reviews received at journal 04 Sep, 2024 Reviewers agreed at journal 03 Sep, 2024 Reviewers invited by journal 02 Sep, 2024 Editor assigned by journal 02 Sep, 2024 Editor invited by journal 23 Aug, 2024 Submission checks completed at journal 22 Aug, 2024 First submitted to journal 22 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4783399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":355139803,"identity":"992c2de8-bca0-4dd3-98b1-e08a81a718cd","order_by":0,"name":"Anis Bensadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFACxgZmCCOx8QFUyIBoLc0GUNWEtDAwQLUksEkQpYV/dnPz54KKO/b87cltlT/+/ElsYG/eJsFQcwenFok7B9ukZ5x5ljjjzMO22zw8BokNPMfKJBiOPcNtzY3ENmbetsMJIMZtBgmgFokcMwnGhsM4dcjfSGz+zPvvsD2Q0Vb4wwCoRf4Nfi0GNxIbpHkbDjNuAGph4EkA2cKDX4shUKX0jGOHEzeeedgszXPA2LiNJ63YIuEYbi1yN9Iffy6oOWwvdzz94ccff+Rk+9kPb7zxoQa3FkzABiISSNAwCkbBKBgFowATAACejVm/pCasFwAAAABJRU5ErkJggg==","orcid":"","institution":"Shenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Anis","middleName":"","lastName":"Bensadi","suffix":""}],"badges":[],"createdAt":"2024-07-22 16:44:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4783399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4783399/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-81173-9","type":"published","date":"2024-11-26T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65084389,"identity":"df686fc1-371c-4499-b057-c0ce49a30a0f","added_by":"auto","created_at":"2024-09-23 12:35:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistogram of Residuals\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4783399/v1/c653f176748d781413a83e42.png"},{"id":65085382,"identity":"b909d90a-a6c5-46d5-8156-50bb80f5806f","added_by":"auto","created_at":"2024-09-23 12:43:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePlotting residuals vs Fitted Values\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4783399/v1/3cb6700049fd398a54719ef0.png"},{"id":70031784,"identity":"38bbc124-d4ec-445a-b854-d10413c4f85f","added_by":"auto","created_at":"2024-11-27 16:32:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":811316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4783399/v1/0c56e9f8-fddd-4617-9fd9-18ef0f60ce0b.pdf"},{"id":65084391,"identity":"d3865116-b463-4473-b358-d7fd0a3c943d","added_by":"auto","created_at":"2024-09-23 12:35:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1360502,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4783399/v1/bd92805016b78f0cc9f6e490.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Impact of Renewable Energy Integration on Energy Efficiency within the China-Pakistan Economic Corridor (CPEC)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe China-Pakistan Economic Corridor (CPEC) is a flagship infrastructure development project that aims to enhance economic cooperation and connectivity between China and Pakistan. It includes a network of transportation, energy, and industrial projects spanning across Pakistan, with a focus on improving trade routes, energy infrastructure, and economic development. CPEC is considered a strategic component of China's Belt and Road Initiative (BRI), aiming to stimulate economic growth, create employment opportunities, and foster regional connectivity. The project holds immense significance for both countries, with Pakistan benefiting from infrastructural development and economic growth, and China gaining access to new markets and trade routes. (Ahmad M., 2019). Energy efficiency within the CPEC refers to the adoption of strategies, technologies, and practices aimed at reducing energy consumption while maintaining or improving the level of energy services. This involves optimizing the energy use across various sectors, such as industrial, residential, and transportation, within the scope of CPEC's infrastructure and development projects. CPEC includes industrial projects where energy efficiency measures are implemented, such as adopting high-efficiency equipment, process optimization, waste heat recovery, and advanced manufacturing technologies to reduce energy wastage (Bhatti, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Energy efficiency involves constructing buildings with improved thermal insulation, energy-efficient windows, HVAC systems, and energy-saving lighting systems, as well as retrofitting existing structures to improve their energy performance. Transportation efficiency focuses on developing public transportation, promoting EV usage, and optimizing logistics operations to reduce fuel consumption, with CPEC projects often including EV charging stations. Smart grid technologies are deployed to optimize energy distribution and use, supported by policies and regulatory frameworks that encourage energy-efficient practices, including setting standards and offering incentives (Wang, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The integration of renewable energy sources with energy efficiency measures maximizes benefits, reducing greenhouse gas emissions and operational costs while enhancing competitiveness. Energy efficiency initiatives in CPEC contribute to building local capacity through training programs and initiatives that rise awareness of energy-efficient technologies among stakeholders and the public, ultimately supporting the corridor's sustainable development goals. The CPEC project presents a range of challenges and opportunities in the energy sector, particularly concerning sustainable development, as the predominant reliance on traditional energy sources, primarily fossil fuels, contains environmental risks and undermines sustainability efforts (Sheikh, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, the persistent underperformance of energy infrastructure within the CPEC often results in ongoing energy shortages, hampering economic recovery efforts (Nazir, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, ensuring compatibility of existing infrastructure with energy-efficient technologies and retrofitting solutions may be a significant challenge in implementing energy efficiency measures. Moreover, effectively integrating renewable energy sources with energy efficiency measures requires careful planning and coordination, which may face technical, logistical, and strategic challenges. Addressing these issues through innovation and adaptive practices could establish a framework guiding China's environmental and economic policies (Li, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Finally ensuring the long-term maintenance and sustainability of energy-efficient systems and technologies poses a challenge, as proper upkeep and ongoing support are critical for continued efficiency gains. The optimization of energy efficiency can aid China in addressing two simultaneous challenges: sustainable energy provision and economic growth at reasonable costs. The purpose of the study is to investigate the impact of renewable energies on energy efficiency within the CPEC project to develop a comprehensive understanding of how integrating renewable energy sources to enhance energy efficiency, thereby optimizing resource utilization and promoting long-term sustainability. By providing empirical evidence and actionable insights, the study aims to inform policymakers and stakeholders involved in CPEC, aiding in the design and implementation of effective policies. (Jiang, 2021) Economically, assessing the impact of renewable energies use on energy efficiency can help designing policies leading to cost savings and economic growth. Additionally, energy security can be enhanced by reducing dependency on imported fossil fuels and diversifying the energy mix. The research also aims at providing recommendations related to the use of the necessary technological innovations and advancements, environmental benefits such as reduced greenhouse gas emissions. By filling existing knowledge gaps and promoting greater stakeholder engagement, the study sets the stage for future research and provide a comprehensive analysis of current practices, challenges, and opportunities in the integration of renewable energy and energy efficiency within large infrastructure projects like CPEC.\u003c/p\u003e \u003cp\u003eThe study investigating the impact of renewable energies on energy efficiency within the CPEC project offers several significant benefits. First, enhanced energy sustainability is achieved by understanding how renewable sources can improve efficiency, optimizing resource use, reducing dependence on fossil fuels, lowering greenhouse gas emissions, and contributing to environmental conservation. Second, economic savings are realized as efficient energy use through renewables significantly reduces costs for consumers and businesses, thereby enhancing economic performance, providing operational cost savings, and stimulating regional economic growth. Third, improved energy security is highlighted by reducing reliance on imported and fossil fuels, with the study showcasing ways to boost energy security by creating a more resilient energy system through a diverse mix and optimized energy use. Fourth, the study provides valuable insights for policymakers, aiding the development and implementation of effective strategies and regulations for integrating renewable energy and energy efficiency measures in energy infrastructure projects. Fifth, technological advancements are driven by exploring the synergy between renewable energy and efficiency, leading to innovations in energy management systems and smart grid technologies. Sixth, environmental benefits are evident through reduced air pollution and carbon emissions, contributing to national and international climate goals and improving public health. Seventh, informed infrastructure planning is facilitated by insights from the study, ensuring energy infrastructure projects under CPEC are designed for energy efficiency and compatibility with renewable systems, maximizing benefits and sustainability. Eighth, social and economic equity is enhanced by identifying cost-effective strategies, making renewable energy benefits accessible to broader populations, including underserved and rural communities, thereby promoting social equity and inclusive development. Ninth, stakeholder engagement and awareness are raised, fostering involvement from businesses, governments, and the public in supporting sustainable development initiatives. Finally, the study offers academic and practical contributions by filling literature gaps, adding to the body of knowledge on energy efficiency and renewable energy integration, and providing actionable insights and best practices for similar global projects.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Overview of Renewable Energies in China\u003c/h2\u003e \u003cp\u003eChina's renewable energy capacity more than tripled between 2010 and 2020 and became the world's leader in installed renewable energy capacity, particularly in solar and wind power. By 2022, China's wind and solar capacities had reached 282 GW and 306 GW, respectively, making significant contributions to the global renewable energy market (Khan M. A., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Chinese government has played a crucial role in promoting renewable energy through various policies and initiatives discuss the implementation of the Renewable Energy Law and the 13th Five-Year Plan, which set ambitious targets for renewable energy development. Moreover, government subsidies, feed-in tariffs, and favourable investment policies have provided substantial financial support for renewable energy projects (Zheng, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The \"14th Five-Year Plan\" continues to promote clean energy transitions, emphasizing the importance of technological innovation and infrastructure development. The shift towards renewable energy has had notable economic and environmental impacts in China. Studies such as (Xu, 2018) highlight the positive effects on energy security, job creation, and economic diversification. The renewable energy sector has also contributed to a significant reduction in carbon emissions and air pollutants, improving public health and environmental quality. However, challenges such as grid integration and intermittency of renewable energy sources remain critical issues (Li, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Technological advancements have been pivotal in improving the efficiency and cost-effectiveness of renewable energy technologies. Significant progress in photovoltaic cell efficiency, wind turbine design, and energy storage solutions (Zhao, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, China's investments in smart grid technologies and digitalization are enhancing the integration and management of renewable energy into the national grid (Wang, 2022). Despite remarkable progress, China's renewable energy sector faces several challenges. Grid integration issues, regional disparities in resource distribution, and the need for technological innovations in energy storage are critical areas for future research and policy development (Duan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, balancing economic growth with environmental sustainability remains a complex issue, requiring coordinated efforts at multiple governance levels (Shah, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Renewable Energy Technologies in China\u003c/h2\u003e \u003cp\u003eChina has emerged as the global leader in solar energy, prioritizing the advancement and deployment of photovoltaic (PV) panels while continuously enhancing efficiency and cost-effectiveness. Additionally, there have been significant advancements in wind turbine technology, resulting in the development of larger and more efficient turbines, including the installation of offshore wind farms. Consequently, the contribution of wind energy to the national grid has notably increased (Hussain, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hydroelectric power plays a pivotal role in China's renewable energy strategy, driving advancements in turbine technology and the implementation of integrated small-scale projects aimed at rural development. China's bioenergy technologies are also noteworthy, with progress in biomass gasification and the conversion of agricultural waste into energy to minimize waste and generate power. Research and development efforts in these areas receive substantial funding from both government and private sectors (Jiang, 2021). The Chinese government's commitment to renewable energy development is evident in its five-year plans and the implementation of the Renewable Energy Law, which provides grants and subsidies to spur innovation. Simultaneously, the private sector, driven by profit maximization motives and governmental tax incentives, heavily invests in the advancement of new technologies. This collaborative effort between the public and private sectors highlights a synergistic approach to promoting renewable energy technology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 China\u0026rsquo;s Policies on Renewable Energy and CPEC\u003c/h2\u003e \u003cp\u003eChina has implemented regulatory and institutional mechanisms for renewable energy development within the CPEC, employing tailored measures to promote its advancement. These measures include the enactment of the Renewable Energy Law and the implementation of the 13th Five-Year Plan, specifically designed to foster the development of renewable energy sources such as solar, wind, hydro, and biomass (Cevik, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, these regulations mandate utilities to purchase 100% of the renewable power they produce, reflecting the confidence of the energy market in supporting green energy initiatives. The CPEC's foundation rests upon a national legislative framework and the establishment of bilateral treaties that ensure the creation of renewable energy infrastructure. Projects like the Pakistan UEP Wind Farm and Dawood Wind Farm exemplify the outcomes of this policy, funded, and constructed with the assistance of Chinese expertise and capital. These initiatives not only contribute to the comprehensive realization of China's environmental objectives, such as emission reduction and power source diversification with increased reliance on non-fossil fuels, but also align with China's economic goals of ensuring secure and sustainable energy supplies for its rapidly expanding industries while promoting the export of renewable energy technologies (Creutzig, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition to the policies, the Belt and Road Initiative (BRI) represents a multilateral development strategy aimed at enhancing regional connectivity and integration for economic purposes. The synergy between China's domestic economic endeavours and its international economic policies is underscored by this initiative.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Importance of Energy Efficiency\u003c/h2\u003e \u003cp\u003eEnergy efficiency is vital for minimizing energy waste, reducing greenhouse gas emissions, and lowering energy costs. Energy efficiency improvements could account for nearly half of the reductions required to meet global climate targets. Enhanced energy efficiency can also alleviate pressure on energy supply systems and contribute to sustainable development (Duan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Technological advancements have played a crucial role in improving energy efficiency across various sectors, including residential, industrial, and transportation. The development of smart grids, which optimize energy distribution and consumption through advanced monitoring and control systems. Additionally, the advent of high-efficiency appliances and industrial processes has led to significant energy savings (Worrell, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Innovations in building materials and design are leading to substantial energy savings. For instance, passive technologies and zero-energy buildings are becoming increasingly feasible due to advancements in insulation, and HVAC systems (Wei Y. e., 2021). The integration of Internet of Things devices in building automation systems allows for real-time energy management and further efficiency gains (Tang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Effective policy measures and regulatory frameworks are essential for promoting energy efficiency. Policies that provide financial incentives, such as tax credits, rebates, and grants, have been shown to accelerate the adoption of energy-efficient technologies (Shan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mandatory energy performance standards and labelling programs help consumers make informed decisions and stimulate market demand for high-efficiency products (Rizvi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Improving energy efficiency has both economic and environmental benefits. Investments in energy efficiency can result in significant cost savings for businesses and consumers. These savings often outweigh the initial investment costs, making energy efficiency financially attractive (O\u0026rsquo;Dwyer, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Environmentally, enhanced energy efficiency contributes to significant reductions in greenhouse gas emissions and air pollutants. Energy efficiency improvements in industries have led to substantial emissions reductions, aiding in the fight against climate change. In the industrial sector, (Worrell, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) provide evidence that energy efficiency measures, such as process optimization and the adoption of advanced manufacturing technologies, can drastically reduce energy consumption and costs. The transportation sector has also seen improvements, with electric and hybrid vehicles playing a key role in enhancing fuel efficiency (Longhurst, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite significant progress, numerous challenges remain. Energy efficiency improvements often face barriers such as high upfront costs, insufficient financing options, and a lack of awareness or information (Lacey-Barnacle, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Overcoming these barriers requires comprehensive strategies that include robust policy support, stakeholder engagement, and continuous technological innovation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Comparative Studies\u003c/h2\u003e \u003cp\u003eThe comparative analysis conducted has provided valuable insights into how the energy efficiency practices within the CPEC, under China's framework, measure up against international examples and regional practices within China itself (Irshad, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). China's preference for centralized approaches has facilitated swift implementation and scaling up of renewable energy projects. A regional examination within China reveals variations in the adoption and energy efficiency policies. Coastal regions like Jiangsu and Guangdong, boasting robust industrial bases and higher economic outputs, have witnessed rapid adoption of green technology and substantial investments in renewable energy projects, unlike inland regions progressing at a different pace. Such disparities underscore the complexity of policy implementation and present both challenges and opportunities for policymakers to navigate diverse economic landscapes across the country. On a global scale, China's commitment to CPEC demonstrates how energy efficiency management can accelerate the transition to renewable energy in developing countries. This approach contrasts sharply with the U.S. model, which leans more heavily on private investment, highlighting the diverse applications of energy management worldwide (O\u0026rsquo;Dwyer, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gaps in Existing Literature\u003c/h2\u003e \u003cp\u003eThe existing literature on energy efficiency initiatives within the CPEC often underscores the immense potential for sustainable development brought by this extensive infrastructure project. Studies addressing energy efficiency within CPEC initiatives, often focusing on the potential of energy savings in industrial and residential sectors (Khan M. S., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, a notable gap in the literature is the limited to the examination of specific energy efficiency measures tailored for CPEC\u0026rsquo;s infrastructure projects. Furthermore, the relationship between energy efficiency improvements and the performance of renewable energies under CPEC is not well-established. This research addresses this gap by employing a quantitative approach by investigating the impact of renewable energies on energy efficiency with the CPEC project, which has not been utilized in prior studies. The approach for this study incorporates unique aspects that are specifically applicable to the CPEC project, offering new insights. The study investigating the impact of renewable energies on energy efficiency within CPEC can offer comprehensive benefits across economic, environmental, social, and technological dimensions, ultimately contributing to more sustainable and resilient energy systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Research Design\u003c/h2\u003e\n \u003cp\u003eThe methodology employed in this research adopts a quantitative approach to conduct a comprehensive examination of the effects of renewable energies on energy efficiency within the framework of the CPEC. Due to the nature of the study, quantitative approach is chosen for its ability to produce objective, reliable, and generalizable results, as it is particularly effective when the research aims to measure variables, investigate statistical correlations between different variables, support robust analysis and informed decision-making. The quantitative analysis entails the collection and examination of numerical data pertaining related to energy efficiency (Olabi, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The objectives of this study can be achieved by applying a regression model and a correlation analysis to analyze these datasets, thereby identifying predominant patterns, and establishing connections between energy efficiency, and renewable energies employed within the CPEC such as hydro, biofuel, solar PV, and geothermal. Through this analysis, the study aims to evaluate impact renewable energies on energy efficiency.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Data Collection\u003c/h2\u003e\n \u003cp\u003eThe data used to achieve the purpose of this study has been collected from the following sources:\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eKaggle\u003c/span\u003e: Provides extensive range of datasets on renewable energy inputs and outputs, including crucial indicators such as investment figures, innovations, and quantitative results like energy generated and conserved.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGlobal Data Repository on Renewable Energy\u003c/span\u003e: This source provides records of renewable energy projects worldwide, including project funding and efficiency ratings, which can be compared with those of CPEC.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCPEC Official Database\u003c/span\u003e: Established by the involved governments, this database offers information on the size, investment amounts, and technology types of CPEC\u0026rsquo;s energy projects.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInternational Renewable Energy Agency (IRENA) Database\u003c/span\u003e: IRENA provides comprehensive data on renewable energy generation, government policies, and financial support for integrating renewable energy into the energy mix, supplementing the analysis of CPEC projects. The dataset chosen encompasses relevant and effective data pertinent to the study\u0026apos;s scope. Key factors explored within the dataset include levels of investment in green technologies, exploration of innovative solutions in renewable energy, and measurable outcomes such as energy production, efficiency, and sustainability (Nazir, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the dataset incorporates details related to the primary country under examination, specifically focusing on CPEC project specifics within the defined timeframe.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Mathematical Model\u003c/h2\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eDependent Variable\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy Efficiency Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eEnergy output/input, also known as the energy efficiency ratio or coefficient, is a measure of how effectively a system converts input energy into useful output energy (Al-Shetwi, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is calculated as the ratio of energy output to energy input. It is determined by the amount of energy produced by the system relative to the amount of energy used by the system. EER (Energy Efficiency Ratio) is calculated using the following formula\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{E}\\text{n}\\text{e}\\text{r}\\text{g}\\text{y}\\:\\text{E}\\text{f}\\text{f}\\text{i}\\text{c}\\text{i}\\text{e}\\text{n}\\text{c}\\text{y}=\\frac{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{R}\\text{e}\\text{n}\\text{e}\\text{w}\\text{a}\\text{b}\\text{l}\\text{e}\\:\\text{E}\\text{n}\\text{e}\\text{r}\\text{g}\\text{y}\\:\\text{O}\\text{u}\\text{t}\\text{p}\\text{u}\\text{t}\\:\\left(\\text{T}\\text{W}\\text{h}\\right)\\text{}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{R}\\text{e}\\text{n}\\text{e}\\text{w}\\text{a}\\text{b}\\text{l}\\text{e}\\:\\text{E}\\text{n}\\text{e}\\text{r}\\text{g}\\text{y}\\:\\text{I}\\text{n}\\text{p}\\text{u}\\text{t}\\:\\left(\\text{T}\\text{W}\\text{h}\\right)}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eA higher energy efficiency ratio indicates that a system has effectively converted a larger proportion of the input energy into useful output energy, reflecting a more efficient operation. On the other hand, a lower energy efficiency ratio signifies that a system has wasted more input energy and produced less useful output energy, indicating lower efficiency. Energy output/input is a crucial metric used in various industries and systems to evaluate the performance and efficiency of energy conversion processes, appliances, equipment, and overall energy systems. Increasing energy efficiency is essential for cost savings, environmental sustainability, and overall system optimization.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eIndependent Variables\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHydro (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis variable quantifies electricity from hydroelectric power that is generated by harnessing the energy of flowing or falling water. This power is generated by converting the kinetic energy of flowing water into electrical energy using turbines.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBiofuel (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis variable quantifies biofuel derived from biological materials, often referred to as biomass. These materials can include plant matter, animal waste, and other organic substances. Biofuels are used as alternatives to fossil fuels in various applications, such as transportation, heating, and electricity generation and are considered as renewable energies.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSolar PV (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis variable quantifies the electricity generated from Solar PV (Photovoltaic) technology that converts sunlight directly into electricity using semiconductor material.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeothermal (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis variable quantifies the energy source that harnesses the heat stored beneath the Earth\u0026apos;s surface to generate electricity or provide heating and cooling for buildings.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMathematical equation\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe regression model is designed to assess the relationship between \u0026quot;Energy Efficiency\u0026quot; (dependent variable) and energy production from various renewable sources (independent variables):\u003c/p\u003e\n \u003cp\u003eEnergy Efficiency Ratio = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:0\\:+\\:\\beta\\:1\\:\\left(Hydro\\:TWh\\right)\\:+\\:\\beta\\:2\\:\\left(Biofuel\\:TWh\\right)\\:+\\:\\beta\\:3\\:\\left(Solar\\:PV\\:TWh\\right)\\:+\\:\\beta\\:4\\:\\left(Geothermal\\:TWh\\right)\\:+\\:\\epsilon\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;₀ (Intercept)\u003c/strong\u003e: Represents the baseline level of energy efficiency when all independent variables are zero.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;₁, \u0026beta;₂, \u0026beta;₃, \u0026beta;₄ (Coefficients)\u003c/strong\u003e: Measure the changes in energy efficiency associated with each one-unit change in the independent variables.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026epsilon; (Error Term)\u003c/strong\u003e: This term captures variations in energy efficiency that the independent variables cannot explain. Whereas E(\u003cstrong\u003e\u0026epsilon;\u003c/strong\u003e)\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThis model seeks to quantify the impact of various renewable energy sources on energy utilization efficiency. It aims to examine the impact of renewable energies such as hydro, biofuel, solar PV, and geothermal on energy efficiency within the CPEC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eThe Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e below represents the descriptive statistics of the dependent variable and independent variables during a timeframe from 2000 to 2022.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Statistics of the dataset 2000\u0026ndash;2022\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHydro (TWh)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBiofuel (TWh)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSolar PV (TWh)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGeothermal (TWh)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEnergy Efficiency\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1190.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1070.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1307.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e324.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHydro (TWh\u003c/span\u003e \u003cem\u003e)\u003c/em\u003e Hydroelectric power demonstrates a relatively high mean generation level of 1190.22, accompanied by a standard deviation of 70.51. The range spans from the lowest value of 1070.85 to the highest value of 1307.89 indicates that the production of power is not stable, these fluctuations can be attributed to various factors such as annual rainfall and water availability (Khan et al., 2018), underscoring their potential influence on hydroelectric output.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eBiofuel (TWh\u003c/span\u003e \u003cem\u003e)\u003c/em\u003e Biofuel production exhibits a mean at 294.01 with a standard deviation of 17.30 indicating less variability. The range of wind farm power generation capacity, spanning from 265.87 TWh to 324.36 TWh, suggests sustained production at a high level, likely attributable to policy support and the abundant availability of biomass resources.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSolar PV\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e(TWh)\u003c/span\u003e The solar PV mean production is considerably lower at 79.06. The standard deviation is small at 4.42, meaning a rather stable trend in solar PV deployment, possibly due to technological progress and cost decline.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGeothermal\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e(TWh)\u003c/span\u003e Geothermal energy, conversely, exhibits the smallest mean production at 0.12 TWh, coupled with an exceedingly low standard deviation of 0.007, indicating highly stable production levels that remain close to what is expected. This suggests that geothermal energy plays a marginal role in China\u0026apos;s renewable energy agenda, likely influenced by geographical constraints.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEnergy Efficiency\u003c/span\u003e The mean energy efficiency stands at 1.16, with a standard deviation of 0.08. This metric signifies the efficiency of energy conversion from production to usable energy, ranging from 0.98 to 1.39, indicating variations in efficiency among sectors or regions. The range implies existing mechanisms for optimizing energy efficiency yet underscores the need for further improvement in maximizing energy efficiency. The descriptive statistics provide a groundwork for understanding the scales and distributions of renewable energy sources in China (Bhatti, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The relatively stable production levels observed across most energy types, apart from hydro, which exhibits some variability, suggest a consistent development trajectory within China\u0026apos;s renewable energy sector amidst relatively unchanged policy environments. This observation serves as a prelude to further investigation into the interactions between these energy sources and economic variables, as well as their impact on energy efficiency.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Regression Analysis\u003c/h2\u003e\n \u003cp\u003eThe research objective is to investigate the impact of renewable energies such as Hydro, Biofuel, Solar PV, and Geothermal, on energy efficiency, using CPEC data from 2000 to 2022. Linear regression analysis relies on several key assumptions that are essential for ensuring the validity and reliability of its results. Understanding and verifying these assumptions for accurate model interpretation and prediction is essential. These assumptions are as follows:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLinear Relationship\u003c/span\u003e: The fundamental premise of multiple linear regression is that there is a linear relationship between the dependent (outcome) variable and the independent variables. This linearity can be visually assessed using scatterplots in the \u003cspan class=\"InternalRef\"\u003e\u003cstrong\u003eAppendix\u003c/strong\u003e\u003c/span\u003e displaying a straight-line relationship rather than a curvilinear one.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMultivariate Normality\u003c/span\u003e: The analysis assumes that the residuals (the differences between observed and predicted values) are normally distributed. This assumption is evaluated by examining histograms or Q-Q plots of the residuals included in the \u003cspan class=\"InternalRef\"\u003e\u003cstrong\u003eAppendix\u003c/strong\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNo Multicollinearity\u003c/span\u003e: It is crucial that the independent variables are not excessively correlated with each other, a condition known as multicollinearity. This can be assessed using correlation matrices in the \u003cspan class=\"InternalRef\"\u003e\u003cstrong\u003eAppendix\u003c/strong\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThis analysis below takes all the assumptions above to answers the research objective and presents the findings from the regression models for the period 2000\u0026ndash;2022.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRegression Results\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe first regression model analyzes how hydro, biofuel, solar PV, and geothermal energy production affect energy efficiency. Here below are the results from the regression analysis shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e as follow:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRegression Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eStandardized\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydro (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.642\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.320\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiofuel (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.580\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.165\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolar PV (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.235\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.496\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeothermal (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eHydro (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe coefficient is -7.642\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a highly significant negative impact on energy efficiency. This suggests that an increase in hydroelectric power production is associated with a decrease in energy efficiency, possibly due to losses in transmission and storage.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBiofuel (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe coefficient is -9.580\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating a significant and negative effect on energy efficiency. This suggests that biofuel production adversely impacts energy efficiency. This could be attributed to the inherently energy-intensive nature of biofuel production, which may render it less efficient compared to other energy sources.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSolar PV (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe coefficient for Solar is -5.235\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (p\u0026thinsp;=\u0026thinsp;0.538), indicating it is not statistically significant. This suggests that increases in energy produced from solar PV do not significantly affect energy efficiency. Thus, at its current scale of implementation, solar PV appears to have a neutral impact on efficiency.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeothermal (TWh)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe coefficient for Geothermal is -0.6550663 (p\u0026thinsp;=\u0026thinsp;0.214), indicating it is not statistically significant. This lack of significant impact could be due to the relatively small scale of geothermal energy production compared to other energy sources.\u003c/p\u003e\n \u003cp\u003eThe results indicate that hydroelectric and biofuel productions have a significant negative correlation with energy efficiency. In contrast, solar PV and geothermal production have an insignificant relationship with energy efficiency. These findings underscore the importance of evaluating renewable energy sources as they are scaled up and integrated into the energy grid.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Correlation Analysis\u003c/h2\u003e\n \u003cp\u003eThe correlation matrix gives useful information on the interconnectivity of the main variables in the dataset, as presented on Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e below:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePearson\u0026apos;s r\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydro (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiofuel (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydro (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolar PV (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydro (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeothermal (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydro (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiofuel (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolar PV (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiofuel (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeothermal (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiofuel (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolar PV (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeothermal (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolar PV (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeothermal (TWh)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"10\"\u003e\n \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHydro (TWh) and Energy Efficiency (-0.632, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe negative correlation observed between the volume of hydroelectric power produced and energy efficiency suggests a significant relationship. This may indicate that higher hydroelectric power generation tends to coincide with lower efficiency in energy utilization. Such a correlation could be attributed to the scale of hydro projects, which might result in considerable energy loss during transmission or inefficiencies in conversion technologies.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eBiofuel (TWh) and Energy Efficiency (-0.222, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe correlation between biofuel production and energy efficiency is moderately negative yet statistically significant. This suggests that higher levels of biofuel energy production moderately diminish energy efficiency. This trend could be ascribed to the less efficient conversion of biofuels into usable energy compared to other renewable sources, or perhaps to the energy expended in the production process of biofuels themselves.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSolar PV (TWh) and Energy Efficiency (-0.027, p\u0026thinsp;=\u0026thinsp;0.638)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe correlation appears to be very weak and lacks statistical significance, suggesting that fluctuations in solar PV energy production have minimal to no effect on energy efficiency at the national level. This observation may imply that while solar PV technology contributes to reducing dependence on fossil fuels, its impact on the overall efficiency of energy is negligible.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eGeothermal (TWh) and Energy Efficiency (-0.014, p\u0026thinsp;=\u0026thinsp;0.8059)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eSimilarly to solar PV, the correlation between geothermal energy production and energy efficiency is very weak and lacks statistical significance, suggesting a negligible impact. This might be attributed to the comparatively smaller scale of geothermal energy production in relation to other energy sources.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSolar PV TWh and Geothermal TWh (-0.114, p\u0026thinsp;=\u0026thinsp;0.0475)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThis moderate negative correlation suggests that regions or periods with higher solar PV production might experience slightly lower geothermal production, or vice versa. This can be interpreted as a reflection of resource allocation preferences within renewable energy portfolios, wherein investments may shift from one source to another based on economic or environmental considerations.\u003c/p\u003e\n \u003cp\u003eThe correlation analysis demonstrates that the most prominent negative correlations in energy efficiency are associated with hydroelectric and biofuel productions. This insight highlights specific areas where policy interventions could be directed, such as enhancing the transmission efficiency of hydroelectric power or refining biofuel conversion technologies. The relatively minor impact of solar and geothermal sources may stem from their current limited scale or integration inefficiencies, suggesting potential avenues for further development and research.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Residual Analysis\u003c/h2\u003e\n \u003cp\u003eResidual analysis is conducted at the end of the study to validate the assumptions underlying the linear regression model. Histograms of residuals and scatter plots of residuals against the independent variables and fitted values are examined. The fitted values are visualized graphically to check for normal distribution of the residuals and to identify any issues related to heteroscedasticity and outliers.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHistogram of Residuals\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe histogram of residuals is presented on Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e below:\u003c/p\u003e\n \u003cp\u003eThe histogram of residuals, overlaid with a normal curve, is utilized to assess the normal distribution of residuals, a fundamental assumption in linear regression analysis. The histogram displays a distribution of residuals that is approximately symmetrically centered around zero. The superimposed normal distribution curve indicates that the residuals closely approximate a normal distribution, although minor deviations are observed. Some bins exhibit slight overcrowding, notably around \u0026minus;\u0026thinsp;0.1 and 0. While not as pronounced as in film, these deviations are still noticeable. Overall, the shape and spread of the histogram reasonably meet the assumption of normal distribution. Therefore, statistical conclusions drawn from the regression analysis are expected to be reasonably accurate under the assumption of normality.\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003ePlot of Residuals vs. Fitted Values\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ePlotting residuals against fitted values is presented on Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e below:\u003c/p\u003e\n \u003cp\u003ePlotting residuals against fitted values provides an additional method to examine for constant variance (homoscedasticity) across all levels of fitted values, a crucial assumption for linear regression. The scatter plot displays randomly distributed residuals along the horizontal axis, indicating no discernible pattern. This absence of systematic errors is a positive indicator. Moreover, the plot does not reveal any discernible trend or curve, suggesting that the model adequately captures nonlinearities. However, there is a slight increase in residual variance for larger fitted values, possibly indicating heteroscedasticity. This observation suggests that the variance of residuals may not be consistent across all levels of fitted values. The approximately normal distribution of residuals strengthens the veracity of the model by providing the basis for the standard tests of coefficients, which are based on the normality assumption. This suggests that t-tests and F-tests can be used in regression analysis as they are suitable. The relatively larger size of residuals at higher fitted values suggests that the data may be heteroscedastic. This, in turn, may lead to the standard errors of the regression coefficient being less reliable, which in turn may lead to the hypothesis tests and confidence intervals being less reliable. One potential solution is the use of robust standard errors to correct for inference procedures in the presence of heteroscedasticity thus ensuring that the statistical conclusions are more reliable. The model provides a way of testing for the homoscedasticity assumption, and the possibility of heteroscedasticity should be subjected to further research, for instance, using tests created for this purpose or by applying corrective measures such as heteroscedasticity-consistent standard error estimators.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion of Findings","content":"\u003cp\u003eThe study's findings offer valuable insights into the impact of renewable energies such as hydro, biofuel, solar PV, and geothermal on energy efficiency within the CPEC. The significant negative correlations observed between hydro and biofuel energy production and energy efficiency suggest that expansion in these sectors does not necessarily lead to more efficient energy use. This could be attributed to inefficiencies in conversion systems or losses during energy transmission. Hydroelectric projects, due to their large scale, generate substantial energy but may face challenges in effective energy distribution and utilization. Similarly, the biofuel industry, while transformative, may require enhancements in energy-intensive processes involved in converting biological materials into usable fuel. In contrast, the insignificant results regarding solar PV and geothermal energy indicate that these technologies are still in the early stages of adoption. Further scaling up is needed to discern their impact on energy efficiency. This presents an opportunity for future technology implementation and development to enhance their contributions to the energy mix. The study's findings align with existing literature, which often underscores the complexities of balancing energy production improvements with efficiency. Previous research has highlighted instances where technological advancements outpaced production, leading to energy wastage and inefficiencies, such as transmission losses in hydroelectric power and low conversion efficiency in biofuels (Longhurst, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The findings challenge certain literature suggesting that newer technologies like solar PV and geothermal should immediately decrease energy consumption due to their lower marginal costs and reduced environmental impacts (Khan M. A., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This discrepancy can be attributed to the nascent stage of technology adoption within the CPEC framework, which has yet to yield significant improvements in infrastructure and integration. These findings highlight the importance of a dual focus in renewable energy policy: not only increasing production but also reducing energy consumption. This approach is essential for maximizing the benefits of renewable energy investment, particularly in the realm of energy management, where the objectives encompass both environmental sustainability and economic prosperity. The insights gleaned from the CPEC extend beyond its specific context and can inform renewable energy initiatives in other regions and countries. The findings underscore the need for future funding in technological development, particularly in energy efficiency and waste reduction, which are pivotal aspects of global change mitigation. The study illustrates the intricate interplay among renewable energy generation, technological innovation, and energy efficiency. While the development of renewable energy capacities remains crucial, the conclusions suggest an equal emphasis on enhancing energy efficiency. The study advocates for further research and policy adjustments to address the identified limitations and unlock the full potential of renewable energy technologies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research study serves as a foundational exploration into the impact of renewable energies that are on energy efficiency. Through comprehensive statistical analysis, the study identifies key weaknesses in emerging renewable energies, offering valuable insights for policymakers and stakeholders. It reveals that hydropower and biofuels, exhibit low efficiency, potentially conflicting with sustainability goals. Conversely, solar PV and geothermal energies, show limited efficiency impacts, suggesting underutilization or integration challenges. These findings highlight the need for policymakers to carefully balance renewable energy development with energy efficiency considerations. The study underscores the importance of energy efficiency, advocating for investments not only in expanding renewable energy capacity but also in enhancing technological efficiencies. Technological innovation emerges as a critical driver for bridging existing efficiency gaps in renewable energy systems. It emphasizes the necessity of continued capital investment alongside a dual focus on adopting new technologies and improving efficiency. These strategic investments are pivotal for long-term energy system development, contributing to energy security and environmental sustainability. The insights provided aim to inform ongoing enhancements to CPEC strategies, ensuring that renewable energy remains a cornerstone of sustainable development.\u003c/p\u003e"},{"header":"6. Recommendations","content":"\u003cp\u003eThe study concludes by offering strategic recommendations to address inefficiencies in the Chinese renewable energy sector within the framework of the CPEC. These guidelines aim to optimize existing strategies, introduce new technologies, and enhance research and development efforts to collectively elevate energy efficiency levels. The inefficiencies observed in the hydroelectric and biofuel sectors are significant and require immediate attention. The primary strategy involves modernizing infrastructure by replacing outdated facilities with new ones equipped with advanced technology aimed at improving energy conversion and transmission efficiency. Additionally, integrating advanced automation and control systems should enhance processes and minimize losses. Improved maintenance practices, can significantly extend the lifespan and enhance the performance of facilities, thereby increasing energy output and reducing costs. For technologies such as solar PV and geothermal, which currently have minimal impact on efficiency, scaling up implementation is crucial. This can be achieved by establishing appropriate policies and taxation schemes to attract both private and public investments. Integrating smart grid technologies would significantly enhance the effectiveness of the renewable sources in question by improving energy distribution management and integrating diverse energy sources. The development of energy efficiency is an ongoing process that requires continual improvement through increased investment in research and development. This investment should focus on creating more effective energy-saving technologies and enhancing existing ones. Partnerships between academia, research institutions, and the energy industry shall be pivotal in developing customized solutions to meet the unique needs of the renewable energy sector. It is, therefore, essential to collaborate with policymakers, industry leaders, and other stakeholders to ensure that China's renewable energy efforts align with national energy needs and global sustainability goals.\u003c/p\u003e"},{"header":"7. Limitations of the Study","content":"\u003cp\u003eThis study aims to conduct a comprehensive analysis of renewable energy efficiency within the CPEC. However, it acknowledges few limitations that may affect its results. A primary concern is the completeness and accuracy of the data. For instance, the dataset may focus on specific types of energy production or provide only general time data. While detailed descriptions of specific technologies and energy efficiency may be available, the dataset may not capture the complex effects of different technologies on energy efficiency. This could result in a skewed understanding of the energy sector and lead to incorrect decisions regarding the utilization of energy sources. Another limitation is the modeling of econometric model used in the analysis. This model is designed to analyze the relationship between energy sources and energy efficiency but may not account for the impacts of other factors such as external economic conditions, policy changes, or environmental factors are often excluded from the models, potentially limiting their ability to accurately represent real-world energy systems. Despite these limitations, the study still provides valuable insights into renewable energy production within CPEC. Future research should aim to address these limitations by incorporating more comprehensive data and employing advanced modeling methods to better capture the complexity of renewable energy systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eThe whole study was generated, conducted by the author Anis Bensadi.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eData would be made available by request from the author Anis Bensadi.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbid, M. \u0026amp;. (2018). CPEC: Benefits for Pakistan, challenges ahead. \u003cem\u003eAsian Journal of International Studies\u003c/em\u003e, 16(2), 131-151.\u003c/li\u003e\n \u003cli\u003eAhmad, S. (2019). Energy efficiency in buildings: A case study of Pakistan. Energy and Buildings. \u003cem\u003eJournal Energy and Buildings\u003c/em\u003e, 196, 30-39.\u003c/li\u003e\n \u003cli\u003eAl-Shetwi, A. Q. (2022). Sustainable development of renewable energy integrated power sector: Trends, environmental impacts, and recent challenges. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, 822, 153645.\u003c/li\u003e\n \u003cli\u003eBhatti, H. M. (2020). Renewable energy deployment under CPEC in Pakistan: Energy security or environmental concern? \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e, 123, 109777.\u003c/li\u003e\n \u003cli\u003eChen, G. Q., et al. (2020). Environmental benefits of renewable energy in China: A lifecycle perspective. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 263, 121280.\u003c/li\u003e\n \u003cli\u003eChen, G. Q. (2019). Environmental benefits of energy efficiency improvements in China: A lifecycle perspective. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 239, 117993.\u003c/li\u003e\n \u003cli\u003eCevik, S. (2024). Climate change and energy security: the dilemma or opportunity of the century? \u003cem\u003eEnvironmental Economics and Policy Studies\u003c/em\u003e, 1-20.\u003c/li\u003e\n \u003cli\u003eCherp, A. V. (2018). Integrating techno-economic, socio-technical and political perspectives on national energy transitions: A meta-theoretical framework. \u003cem\u003eEnergy Research \u0026amp; Social Science\u003c/em\u003e, 37, 175-190.\u003c/li\u003e\n \u003cli\u003eCreutzig, F. R. (2018). Towards demand-side solutions for mitigating climate change. \u003cem\u003eNature Climate Change\u003c/em\u003e, 8(4), 260-263.\u003c/li\u003e\n \u003cli\u003eDuan, W. K. (2022). Pakistan\u0026apos;s energy sector\u0026mdash;from a power outage to sustainable supply. Examining the role of China\u0026ndash;Pakistan economic corridor. \u003cem\u003eEnergy \u0026amp; Environment\u003c/em\u003e, 33(8), 1636-1662.\u003c/li\u003e\n \u003cli\u003eHussain, M. e. (2018). Environmental impacts of renewable energy projects under CPEC. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, 25(21), 20435-20449.\u003c/li\u003e\n \u003cli\u003eIrshad, M. S. (2019). The potential for economic growth through CPEC\u0026rsquo;s energy projects. \u003cem\u003eEconomic Research\u003c/em\u003e, 32(1), 203-219.\u003c/li\u003e\n \u003cli\u003eJiang, P. e. Extended water-energy nexus contribution to environmentally related sustainable development goals. Renewable and Sustainable Energy Reviews. 145, 111129\u003c/li\u003e\n \u003cli\u003eKabeyi, M. J. (2022). Sustainable energy transition for renewable and low carbon grid electricity generation and supply. \u003cem\u003eFrontiers in Energy Research\u003c/em\u003e, 09.\u003c/li\u003e\n \u003cli\u003eKhan, M. A. (2018). Analysis of power plants in China Pakistan economic corridor (CPEC): An application of analytic network process (ANP). \u003cem\u003eJournal of Renewable and Sustainable Energy\u003c/em\u003e, 10(6).\u003c/li\u003e\n \u003cli\u003eKhan, M. I. (2020). An overview of global renewable energy trends and current practices in Pakistan\u0026mdash;A perspective of policy implications. \u003cem\u003eJournal of Renewable and Sustainable Energy\u003c/em\u003e, 12(5).\u003c/li\u003e\n \u003cli\u003eKhan, M. S. (2020). Energy efficiency policies in the context of CPEC: Status and opportunities. \u003cem\u003eEnergy Policy\u003c/em\u003e, 142, 111551.\u003c/li\u003e\n \u003cli\u003eLacey-Barnacle, M. R. (2020). Energy justice in the developing world: a review of theoretical frameworks, key research themes and policy implications. \u003cem\u003eEnergy for Sustainable Development\u003c/em\u003e, 55, 122-138.\u003c/li\u003e\n \u003cli\u003eLi, X. e. (2019). Renewable energy policies and barriers in the context of CPEC. \u003cem\u003eEnergy Policy\u0026nbsp;\u003c/em\u003e, 128, 120-129.\u003c/li\u003e\n \u003cli\u003eLonghurst, N. \u0026amp;. (2019). Mapping diverse visions of energy transitions: co-producing sociotechnical imaginaries. \u003cem\u003eSustainability Science\u003c/em\u003e, 14(4), 973-990.\u003c/li\u003e\n \u003cli\u003eNazir, A. e. (2020). Assessing the environmental footprint of CPEC\u0026rsquo;s energy projects. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, 192(3), 246.\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Dwyer, E. P. (2019). Smart energy systems for sustainable smart cities: Current developments, trends and future directions. \u003cem\u003eApplied energy\u003c/em\u003e, 237, 581-597.\u003c/li\u003e\n \u003cli\u003eOlabi, A. G. (2022). Renewable energy and climate change. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e, 158, 112111.\u003c/li\u003e\n \u003cli\u003eRizvi, S. e. (2019). Technological advancements in renewable energy solutions under CPEC. \u003cem\u003eEnergy Reports\u003c/em\u003e, 5, 761-771.\u003c/li\u003e\n \u003cli\u003eShah, S. Z. (2021). Top managers\u0026apos; attributes, innovation, and the participation in China\u0026ndash;Pakistan Economic Corridor: A study of energy sector small and medium‐sized enterprises. \u003cem\u003eManagerial and Decision Economics,\u003c/em\u003e, 42(2), 385-406.\u003c/li\u003e\n \u003cli\u003eShan, S. G. (2021). Role of green technology innovation and renewable energy in carbon neutrality: A sustainable investigation from Turkey. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, 294, 113004.\u003c/li\u003e\n \u003cli\u003eSheikh, S. M. (2019). CPEC Investment Opportunities and Challenges in Pakistan. \u003cem\u003eJournal of Accounting and Finance in Emerging Economies\u003c/em\u003e, 5(1), 123-128.\u003c/li\u003e\n \u003cli\u003eTang, B. e. (2021). Electric vehicle infrastructure under CPEC: Development and challenges. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 282, 125437.\u003c/li\u003e\n \u003cli\u003eWei, Y. e. (2021). Overcoming barriers to energy efficiency in China: Policy recommendations and industry perspectives. \u003cem\u003eRenewable Energy\u003c/em\u003e, 178, 108-115.\u003c/li\u003e\n \u003cli\u003eWang, Y., et al. (2022). Smart grids: Enhancing the integration of renewable energy in China. \u003cem\u003eIEEE Access\u003c/em\u003e, 10, 45623-45633.\u003c/li\u003e\n \u003cli\u003eWang, Q. e. (2018). Improving energy efficiency in China\u0026apos;s industrial sector: Current practices and future challenges. \u003cem\u003eApplied Energy\u003c/em\u003e, 225, 685-694.\u003c/li\u003e\n \u003cli\u003eWei, Y. e. (2021). Policy and regulatory barriers to energy efficiency in Pakistan: Contextual analysis under CPEC. \u003cem\u003eEnergy Reports\u003c/em\u003e, 7, 117-124.\u003c/li\u003e\n \u003cli\u003eWorrell, E. e. (2020). Industrial energy efficiency: Technologies and measures for significant savings. \u003cem\u003eEnergy Efficiency\u003c/em\u003e, 13(6), 1037-1056.\u003c/li\u003e\n \u003cli\u003eXu, Y., et al. (2018). Economic impacts of renewable energy in China. \u003cem\u003eApplied Energy\u003c/em\u003e, 231, 56-67.\u003c/li\u003e\n \u003cli\u003eZhao, X. e. (2022). Smart grids and energy efficiency under CPEC: Challenges and opportunities. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e, 152, 111759.\u003c/li\u003e\n \u003cli\u003eZheng, J. e. (2020). Economic benefits of energy efficiency in China: Evidence from industrial and residential sectors. \u003cem\u003eEnergy Economics\u003c/em\u003e, 86, 104696.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Data would be made available by request from the author Anis Bensadi\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Renewable Energies, Energy Efficiency, China-Pakistan Economic Corridor (CPEC), Sustainable Development. ","lastPublishedDoi":"10.21203/rs.3.rs-4783399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4783399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe China-Pakistan Economic Corridor (CPEC) is a major development project that aims to enhance connectivity and cooperation between China and Pakistan. It is part of China's broader Belt and Road Initiative (BRI), which seeks to improve trade routes and economic integration across Asia, Africa, and Europe. The study examines the impact of renewable energies such as hydro, biofuel, solar PV, and geothermal on energy efficiency within the CPEC. The findings reveal significant inefficiencies in the hydroelectric and biofuel energies, where energy production increases while efficiency declines. In contrast, solar PV and geothermal power had no discernible impact on the energy efficiency, suggesting that either these technologies are not yet fully utilized, since still being in the early stages of integration into CPEC's energy grid. The study recommends implementing several practices such as modernizing infrastructure by replacing outdated facilities with advanced technology to improve energy conversion and transmission efficiency. Integrating automation and control systems to minimize losses and enhancing maintenance practices to extend facility lifespan and performance. Scaling up solar PV and geothermal technologies through supportive policies and investments. Integrating smart grid technologies to enhance renewable energy effectiveness through better energy management. Finally, increase research and development investment, with essential collaborations between academia, research, and the energy industry to develop tailored solutions to meet the unique needs of the CPEC project.\u003c/p\u003e","manuscriptTitle":"Assessing the Impact of Renewable Energy Integration on Energy Efficiency within the China-Pakistan Economic Corridor (CPEC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-23 12:35:28","doi":"10.21203/rs.3.rs-4783399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-10T11:45:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-07T16:00:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153400915339687889700766011231411906610","date":"2024-09-17T09:59:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-04T05:51:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277237919179394991564339155901337565167","date":"2024-09-03T05:31:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-02T18:29:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-02T18:03:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-23T11:37:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-22T05:39:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-22T16:43:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f65af67a-c6f6-4f09-8a87-233cfc892b64","owner":[],"postedDate":"September 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37749629,"name":"Physical sciences/Energy science and technology/Energy storage"},{"id":37749630,"name":"Physical sciences/Energy science and technology/Fossil fuels"},{"id":37749631,"name":"Physical sciences/Energy science and technology/Renewable energy"},{"id":37749632,"name":"Earth and environmental sciences/Environmental sciences"},{"id":37749633,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2024-11-27T16:32:15+00:00","versionOfRecord":{"articleIdentity":"rs-4783399","link":"https://doi.org/10.1038/s41598-024-81173-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-26 00:00:00","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-09-23 12:35:28","video":"","vorDoi":"10.1038/s41598-024-81173-9","vorDoiUrl":"https://doi.org/10.1038/s41598-024-81173-9","workflowStages":[]},"version":"v1","identity":"rs-4783399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4783399","identity":"rs-4783399","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}