A Feasibility Study for the Optimized Development of Renewable Air-Conditioning for Different Climatic Conditions

This study examines the impact of capital market on the relationship between energy consumption and carbon emissions. By employing a system Generalised Methods of Moments (GMM) for a sample of 138 developing countries over the period, 1990–2020, we find a U-shaped reverse relationship between renewable energy consumption and carbon emissions. The study reveals that beyond a threshold of 71.03, renewable energy consumption tends to increase carbon emissions. Similarly, the initial levels of carbon emissions reduce the use of renewable energy but beyond a 2.5 level of carbon emissions, renewable energy consumption begins to increase. We find that both the stock market and bond market reduce carbon emissions and enhance the levels of renewable energy consumption. We provide evidence to support that the capital market enhances the negative impact of renewable energy consumption on carbon emissions, while the corporate bond market magnifies the reductive effect of carbon emissions on renewable energy consumption.

Energy consumption and carbon emission levels in the production process constitute an important basis for the selection of production equipment. The energy consumption and carbon emission levels of the same product produced by different kinds equipment vary greatly from one tool to another. Unfortunately, traditional modes of selection only give qualitative results, so that it is difficult to provide a quantitative reference to enable enterprises to choose appropriate modes of production in the context of the green development concept (GDC). In order to solve this problem, a calculation method for multiple energy consumption and carbon-emission objectives for commodity production is proposed. The focus of this paper is to research the difference between the energy consumption and carbon emission levels of the same product produced by different kinds of equipment. The energy consumption and carbon emissions of different kinds of equipment can be calculated by gray wolf algorithm. The results show that the proposed method can calculate the optimal values of energy consumption and carbon emissions in the same kinds of products produced by different equipment, which can provide assistance for enterprises in choosing the production equipment that best conforms to the green development concept.

Upon an improvement in the quality of life, air-conditioning has generally been applied. Nevertheless, environmental and health issues related with the use of air-conditioning occurs more often. Therefore, this paper aims to theoretically assess the principles of sustainability to achieve sustainability for renewable smart air-conditioning. Not only with consideration to the geometry (i.e. system mechanisms and components), fuzzy logic control and proportional-integral-derivative that such studies drawn particular attention to, but with concerns to a matter which has been previously ignored. That is with consideration to the potential which the renewable-based options, advanced smart control technique and profitability measures of air-conditioning reinforces the three pillars of sustainability, and their sustainable indicators as context-specific transformations have, to not only eradicate indoor health effects, lower the levels of energy consumption and rate of carbon emissions, but to uncover the significance of and particular contribution renewables and smart control opportunities makes to the sustainability of the system. In meeting this aim and demonstrating the sustainability of the theoretical framework, this paper reveals renewable and smart control system as the fundamental key components of the air-conditioning as it promotes to reduce levels of energy consumption and lower carbon emissions, vis-à-vis establish a comfortable and healthy indoor environment as an exercise in the sustainable theoretical framework whose status as renewable smart air-conditioning not only tackle poor indoor air quality but also combat global warming and climate change.

Energy saving and emission reduction in higher education institutions is an indispensable part in the context of carbon neutral strategy. Most of the common studies on low-carbon emission reduction in universities are about the optimization and adjustment of campus power grid structure or the metering or load forecasting of campus power and carbon emission data, but the strong coupling between energy consumption and carbon emission and personnel behavior in the special area of universities, which has a high population concentration, high per capita energy consumption and social attributes such as talent cultivation, research and policy birth, has not been considered in detail. The strong coupling between energy consumption and carbon emission and personnel behavior has not been considered in detail. Based on this, we selected the regional building clusters on campus, constructed the corresponding energy and carbon emission models, and analyzed the distribution patterns of energy and carbon consumption levels with the behavior of students by combining the flow patterns and behavior patterns of people on campus. At the same time, a GIS distribution map of campus energy and carbon visualization is established for the behavior trajectory of energy and carbon distribution, and the feasibility of connecting to the campus navigation system is considered, so that the carbon reduction target signal can be transmitted to individuals. The analysis results show that there is a significant correlation between energy and carbon consumption levels and students' activity behaviors, and different behavioral interventions lead to different energy consumption and carbon emission levels; the campus GIS map can represent the energy and carbon distribution in the campus more intuitively. This work also provides a newer perspective on campus carbon reduction.

Recent studies of urban morphology, suggest the design, layout and texture of district centres, neighbourhoods and buildings have as much a bearing on levels of energy consumption and rates of carbon emission as either buildings or their occupation. They suggest urban morphology matters and both the design, layout and texture of district centres, neighbourhoods and buildings are as significant in setting levels of energy consumption and rates of carbon emission as the occupation and use of such structures. This paper aims to reiterate this message and demonstrate how urban morphology does matter. Not only with respect to the geometry (i.e. surface and volume of the building design typologies), construction systems, or occupational behaviours, that such studies drawn particular attention to, but with regards to a matter which they have hitherto overlooked. That is with regards to the potential which the planning, (re)development, design and layout of district centres and their neighbourhoods as context-specific transformations have, to not only lower levels of energy consumption and rates carbon emission, but mitigate the climate change associated with the occupation and use of buildings. In meeting this aim and demonstrating how urban morphology does matter, the paper shall draw upon the experiences of a transformation taking place in the London Borough of Sutton known as the Hackbridge project: a mass retrofit proposal designed as a sustainable suburb with distinct centres, neighbourhoods and buildings, laid out and contextualised as an energy efficient-low carbon zone.

Anthropocene global warming is largely associated with fossil fuel carbon emissions. Temporal scaling provides a way to place current carbon emissions on a geological scale. The scaling of carbon emissions at the onset of hyperthermal events suggests that we might anticipate higher carbon emission rates over longer time scales than what we currently observe in the Anthropocene. However, this inference is uncertain due to limited data concerning the accumulations and time intervals of carbon emissions of Meso-Cenozoic hyperthermal events. While on the long-time hyperthermal-event scales of several to hundreds of kiloyears, modern carbon accumulations and emission rates are 9 times greater than those of the hyperthermal-event emissions. The present-day carbon release can be effectively compared to the onset of hyperthermal events through temporal scaling. If current carbon emission trends persist, we may reach the carbon emission thresholds for hyperthermal events in one to three hundred years, getting an intensified hydrological cycle, enhanced continental weathering and ocean acidification. And if the situation gets worse, we may reach the upper limit of the carbon emission threshold for hyperthermal events (e.g., Permian-Triassic Boundary event, PTB) with a biotic mass extinction over four to thirteen hundred years. This study offers new insights into current carbon emissions from a temporal scale perspective, enhancing our understanding of contemporary climate change.

This study investigates the impact of dirty (gas, oil) and clean (renewable) energy consumption on CO emissions in Azerbaijan. Utilizing annual data from 1985 to 2022, the analysis examines the 2 stationarity of the variables using ADF, Flexible Fourier ADF, and Fractional Flexible Fourier ADF unit root tests, finding that all variables are stationary at the first difference. To explore the cointegration relationships among the variables, the Bayer-Hanck combined cointegration test is employed. Three econometric models are established, revealing a long-run equilibrium relationship solely between renewable energy consumption and carbon emissions (CO ). According to the long-run estimation re- 2 sults from FMOLS, DOLS, and CCR methods, renewable energy consumption negatively impacts carbon emissions in Azerbaijan, with a 1% increase in renewable energy consumption leading to a 0.60% decrease in carbon emissions. Additionally, the Fourier Toda-Yamamoto causality test uncovers a unidirectional causality from gas consumption to carbon emissions in Azerbaijan. The study concludes with policy recommendations for Azerbaijan to effectively manage and reduce carbon emissions

Transport profoundly affects energy use and carbon dioxide emissions in the tourism sector. The Wulingyuan Scenic Area (WSA), a natural heritage destination in China, is chosen for the case study. The energy consumption and carbon emission of 10 types of tourism transportation modes at the destination are measured and analyzed using a bottom‐up approach for the period of 1979 to 2010. Scenarios were created to project the effects of single and multiple factors on energy consumption and carbon emission by tourism transportation during 2011‐2020. The results showed the following: (a) there is a large difference in energy consumption and carbon emission per capita and per kilometer per capita among the 10 vehicle modes; (b) the monthly energy consumption and carbon emission of tourism transportation differed significantly, the month with the highest (October) are respectively 6.8 and 4 times that of the lowest month (January); (c) the highest annual growth rate of energy consumption and carbon emission are respectively as 32.16% and 27.98% during 1979‐2010; and (d) the amount of energy consumption and carbon emission in the multiple factor scenarios are lower than that in the reference and single factor scenarios during 2011‐2020.

Large amount of energy use for rising economic growth leads to high carbon dioxide discharge that worsens environmental quality which is a challenge for countries in achieving sustainable development. Improved level of technological innovations and renewable energy consumption might overcome the issue of environmental degradation and achieving sustainable development. This study examines the effect of technological innovations on renewable energy consumption and carbon dioxide emission in the belt and road initiative countries for the period of 1995 to 2019. Two-step difference and two-step system GMM models were employed for analysis where the results indicate that technological innovations increase renewable energy consumption and carbon dioxide emission. The effect of renewable energy consumption and trademark applications on carbon dioxide is negatively significant that raises environmental quality. Furthermore, this study confirms the validity of Environmental Kuznets curve hypothesis in the sample countries. The findings of this study have considerable policy implication for the sample countries on rising technological innovations and renewable energy consumption in achieving environmental sustainability.

Research on the dynamic relationship between China's renewable energy consumption and carbon emissions based on ARDL model

Enhancing low-carbon building operations leveraging demand response driven by renewable energy consumption contributions

The study examined the dynamic nexus between carbon footprints, nonrenewable energy and renewable energy consumption, financial development and economic growth, and combating climate change by using a dataset of selected 13 Asian emerging economies (Bangladesh, China, India, Indonesia, Iran, Malaysia, Nepal, Pakistan, Philippines, South Korea, Sri Lanka, Thailand, and Vietnam) from 1995 to 2020. This study empirical analysis uses the second generation of panel cointegration techniques to compensate for cross-sectional dependency and slope heterogeneity. The mean group, the common correlated effects mean group, and the augmented mean group are used to estimate the long-run equations. The findings suggest that economic growth and nonrenewable energy consumption exacerbate environmental degradation, but renewable energy consumption mitigates the total adverse effects on the environment over time. Additionally, economy-specific findings examine how the impact of nonrenewable energy and renewable energy consumption on the carbon footprint depends on energy consumption level. Furthermore, the Dumitrescu-Hurlin causality test reveals a statistically significant bidirectional correlation between financial development, carbon footprints, economic growth, and consumption of nonrenewable energy and renewable energy. Finally, the study says that Asian emerging economies should use more renewable energy and be more efficient in order to reduce their carbon footprints.

The establishment of the Gigafactory is a crucial milestone in the worldwide effort to achieve a sustainable energy future. The Gigafactory employs sustainable energy sources to manufacture a battery pack. The implementation of an environmentally conscious and sustainable energy system within the gigafactory has been found to yield favorable outcomes. Non-renewable energy sources pose a significant threat to the environment due to their carbon dioxide emissions. The primary challenge is to minimize carbon dioxide emissions through the use of nonrenewable energy sources during the next ten years. To do this, it is imperative to comprehend the projected global consumption of nonrenewable energy over the next decade. Following the completion of the prediction of the level of consumption for the subsequent ten years, governments will be able to ascertain whether the consumption of renewable energy or nonrenewable energy will be increasing or declining. Predictive models are necessary to determine the anticipated energy consumption levels for future decades in areas where there is a noticeable rise in energy consumption. The study employed the Automatic ARIMA model, a predictive model commonly utilized for forecasting purposes, to anticipate the levels of nonrenewable energy consumption and the absorption of energy from renewable sources. This prediction was carried out using the R package forecast. The research demonstrates that the implementation of Auto ARIMA on historical energy consumption data enables the projection of future non-renewable energy and renewable energy consumption for ten years. Based on the forecast, the consumption level of nonrenewable energy surpasses that of sustainable energy sources.

The aim of the study is to examine the effects of fossil fuel consumption and renewable energy consumption on carbon emissions (CO2). In this context; energy consumption and carbon emission data of European Union countries for the period 1990-2015 are analysed using Descriptive Statistics, Time Series Regression Analysis (Least Squares - OLS) and Heteroskedasticity Control (Huber-White HC1 Correction) methods. According to the results of the regression analysis evaluating the impact of fossil fuel consumption on carbon emissions, an increase in fossil fuel consumption increases carbon emissions at a statistically significant level (p = 0.0000). It was determined that a 1% increase in fossil fuel consumption rate increases carbon emissions by approximately 0.167 metric tonnes. This result clearly demonstrates the role of fossil fuel use in carbon emissions and its environmental damage. This finding is consistent with the direct effect of fossil fuel use on greenhouse gas emissions, which is frequently emphasised in the literature. According to the results of the analysis examining the effect of renewable energy consumption on carbon emissions, the increase in renewable energy use significantly reduces carbon emissions (p = 0.0000). A 1% increase in renewable energy consumption rate leads to a decrease of 0.155 metric tonnes in carbon emissions. This finding proves the undeniable importance of renewable energy sources in combating climate change. The results reveal that renewable energy investments are important not only for energy supply security but also for environmental sustainability. It provides strong evidence that the European Union's renewable energy targets should be raised and policy support mechanisms should be maintained.

With the improvement of living standards, household carbon emissions have exhibited a significant upward trend. In northern regions, the substantial influence of seasonal heating demand results in higher energy consumption and carbon emission levels among households. However, micro-level studies examining the characteristics of per-household carbon emissions and their influencing factors in these areas remain scarce and limited. Consequently, there is an urgent need for more comprehensive research to provide a theoretical foundation for formulating precise carbon emission reduction policies and strategies. this study employs the emission coefficient method to quantify the carbon emissions resulting from energy consumption by urban and rural households in northern regions. The findings indicate that electricity consumption and transportation-related travel are prominent sources of carbon emissions among northern households; additionally, the use of natural gas, liquefied petroleum gas, and other energy sources also contributes significantly to overall emissions. Notably, coal-fired heating has a particularly pronounced impact on household carbon emissions during winter months.