Energy-related Carbon Emissions Research Articles

Multidrivers of energy-related carbon emissions and its decoupling with economic growth in Northwest China

Northwest China has great natural resource endowment to develop its economy, but factors such as geographic remoteness and technological backwardness result in lower economic levels and higher carbon emissions. This study calculated the energy-related carbon emissions of five provinces in this region, and the evolutionary characteristics of energy-related carbon emissions were analysed from the spatiotemporal perspective. The Kaya identity was applied to decompose the factors influencing energy-related carbon emissions, and the logarithmic mean divisia index (LMDI) and refined Laspeyres index were combined to calculate the role of each influencing factor on energy-related carbon emissions. Finally, the Tapio and LMDI models were used to analyse the evolution of the decoupling relationship between energy-related carbon emissions and economic growth and the role of various influencing factors. The energy-related carbon emissions in Northwest China showed an increasing trend. In terms of influencing factors, economic growth and urban expansion had the highest contributions to carbon emissions and decoupling inhibition, whereas population agglomeration had the opposite effect. Northwest China showed great decoupling trends between energy-related carbon emissions and economic growth.

City-level building operation and end-use carbon emissions dataset from China for 2015–2020

sThe building sector, which accounts for over 20% of China’s total energy-related carbon emissions, has great potential to reduce emissions and is critical to achieving China’s emissions peak and carbon neutrality targets. However, the lack of data on operational carbon emissions and end-use carbon emissions in the building sector at the city level has become a major barrier to the development of building energy conservation policies and carbon peaking action plans. This study uses a combination of “top-down” and “bottom-up” methods to account for the operational carbon emissions of buildings in 321 cities in China from 2015 to 2020. The energy consumption in buildings is further broken down into six end uses: central heating, distributed heating, cooking and water heating (C&W), lighting, cooling, appliances and others (A&O). The dataset can serve as a reference to support city-level policies on peak building emissions and is of great value for the improvement of the carbon emissions statistical accounting system.

Energy-related carbon emission reduction pathways in Northwest China towards carbon neutrality goal

The Chinese government has announced ambitious carbon reduction goals to address climate change, however, there has been limited scientific attention to the achievement of deep decarbonization in regions with insufficient economic development. Moreover, the complex impacts of climate change mitigation on energy system, environment and economic development remain unclear in Northwest China. Here we construct a comprehensive evaluation model system to evaluate the effects of different climate mitigation pathways in Qinghai, Ningxia, Gansu and Xinjiang provinces. Results show that the four provinces need to vigorously increase the share of electricity that consumed in final energy to 53.1–60.0% and 52.9–62.0% for 2 °C and 1.5 °C target, respectively, and reduce total energy demand to 11.6–44.4 Mtoe and 8.2–34.1 Mtoe in 2050. Meanwhile, the CO2 emissions for their energy systems will reach peak by 2025 at lower values. Moreover, the synergies of CO2 reduction are most conducive to abating SO2 pollution and also contributing to decreasing PM2.5 concentrations. Additionally, the GDP losses in 2050 fluctuate from 5.0 to 21.7 billion USD in the 2 °C scenario to 11.5–52.7 billion USD in the 1.5 °C scenario. Our study provides practical guidance on achieving carbon reductions in less developed regions of China and lays foundations to promote decarbonization of energy systems in other provinces.

Practical effects of carbon emissions trading system on energy efficiency

The carbon emissions trading system (CETS) is a helpful policy instrument for separating carbon emissions from economic expansion, and it significantly impacts energy efficiency (EE). This study uses 30 Chinese provinces from 2007 to 2020 as its research samples, and classifies energy efficiency into single-factor energy efficiency (SFE) and total-factor energy efficiency (TFE), using the difference-in-differences model to examine the effect and mechanism of the CETS on EE. As an additional tool to assess the efficacy of the CETS, the corresponding evolution of the rebound effect of energy-related carbon emissions (RECE) is also calculated. This study shows that the CETS can significantly improve EE in China's pilot provinces. The influence mechanism indicates that the effect of the CETS on EE is influenced by the level of government governance, green innovation, and industrial structure optimization. Further study finds that after the CETS was carried out, the RECE in pilot provinces was higher than that in non-pilot provinces, and 31.4% of carbon emissions reduced by EE improvement rebounded. Therefore, the CETS has yet to realize its full carbon reduction potential. The study offers specific policy proposals for the enhancement of China's CETS in light of the aforementioned findings.

Integrating sustainable development goals into the architecture curriculum: Experiences and perspectives

The Sustainable Development Goals (SDGs) play a crucial role in architectural education, as buildings contribute 39% of global energy-related carbon emissions and 40% of extracted materials are used in construction. This research investigates the current status of SDG integration in architecture education, its challenges, and potential future advancements. A qualitative survey was conducted among architecture educators from 22 institutions across nine countries, focusing on four key aspects: (i) general knowledge and understanding of the SDGs; (ii) qualification and experience regarding the SDGs; (iii) integration of the SDGs in architecture education; and (iv) implementation of the SDGs in architectural practises. The findings revealed that most educators did not receive formal education focused on the SDGs, relying on self-exposure and self-learning. SDG 11 was the most adopted, focusing on improving slum areas, providing safe housing, and promoting sustainable urban settlements while preserving cultural heritage.

Investigating the fast energy-related carbon emissions growth in African countries and its drivers

Efforts to avoid the acceleration of global warming have tended to focus on countries with high CO2 emissions levels and large populations, with a high level of economic development or industrialization. African countries, which often do not conform to such criteria, are more vulnerable to climate change due to their dependence on climate-sensitive industries and their limited infrastructure and technological capacity to cope with its impacts. The long-term economic growth rates projected for Africa's rapid development period will, further, make Africa a potential emission hotspot in the near future. Here, for the first time, we built an energy-related emissions inventory for 19 African countries for 2010–2019, which addresses emissions from 47 economic sectors and 5 energy types, making it the most comprehensive of its kind. The degree of decoupling of economy and emissions, and drivers of CO2 emission changes are also examined. Most African countries experienced rapid growth in CO2 emissions, with an average annual growth rate of 5.5% for fossil fuel-related CO2 emissions and 6.0% for unsustainable biomass-related CO2 emissions. Only two countries, South Africa and Tanzania, have achieved a strong decoupling of economic growth from CO2 emissions. Economic and population are the most important drivers of emissions, while energy intensity has been identified as a key factor in mitigating CO2 emissions, especially for those countries that have reached strong or weak decoupling. The findings from this study provide essential insights that could guide the development of low-carbon policies and strategies in Africa.

The impacts of driving variables on energy-related carbon emissions reduction in the building sector based on an extended LMDI model: a case study in China.

As China's main contributor to energy-related carbon emissions, the building sector in Jiangsu Province generates around 13.58% of the national carbon emissions. However, the influential variables of the energy structure in Jiangsu Province have been little investigated during the past decade. With the increasing emphasis on China's investment in technological innovation and adjustment of its industrial structure, research and development (R&D) has become an inevitable area for carbon emissions reduction. Nevertheless, its role in carbon emissions has rarely been examined. In this research, based on the logarithmic mean Divisia index (LMDI) model, the variables affecting the fluctuation of carbon dioxide emissions in the building sector (CEBS) in Jiangsu Province during 2011-2019 were restructured by introducing technological factors related to the construction industry, including energy structure, energy intensity, R&D efficiency, R&D intensity, investment intensity, economic output, and population engaged in the construction industry. From the results, it can be inferred that (1) energy structure, energy intensity, R&D efficiency, and investment intensity operate as inhibitors in increasing CEBS, and investment intensity exerts a more prominent impact on suppressing the growth of CEBS; (2) R&D intensity, economic output, and population engaged have a promotional effect on the fluctuations of CEBS, among which the first factor most actively promoted the increase in carbon emissions, although its role was negligible for economic output and the population; and (3) R&D efficiency, R&D intensity, and investment intensity are the three most critical variables for influencing the CEBS, but they are volatile. The numerical fluctuation caused by the three factors might be correlated to national and local policy interventions. Finally, policy recommendations are put forward for strengthening the management and minimizing the CEBS in Jiangsu Province.

Policy Mapping for Net-Zero-Carbon Buildings: Insights from Leading Countries

The construction industry is a key contributor to greenhouse gas emissions, with buildings alone accounting for 39% of the global energy-related carbon emissions. Global carbon emissions from building operations increased by 5% in 2021 compared to 2020. However, the United Nations signed the Paris Climate Agreement in 2015 with global leaders, setting a limit to temperature increases below 2.0 °C or 1.5 °C. To achieve this goal, countries have established net-zero targets to reach carbon neutrality by mid-century. However, while some countries are making significant progress, others lag behind. Therefore, this study focuses on evaluating the actions taken by countries toward carbon neutrality, and on developing a policy roadmap for the construction industry to meet the net-zero-carbon commitments. This research adopted a systematic document review, including document analysis. The evaluation of countries’ practices towards achieving net-zero targets reveals both similarities and differences. The policy maps developed can be customised for decarbonising a country’s overall construction industry and building sector. This study provides insights for research, practice, and society, emphasising the importance of achieving net-zero targets through the implementation of policies, roadmaps, plans, and strategies.

92 ‘Greenprint’ for integrated care clinics—the hub and spoke model

Abstract Background Climate change is a major public health crisis requiring attention at an individual, local and national policy level. Transport is the largest source of energy related carbon emissions in Ireland. The development of integrated care occurs alongside climate emergency. Care previously delivered in hospital outpatients, or a central ‘hub’ is now being delivered in integrated care clinics, ‘spokes’. We aimed to assess the environmental impact of transport to integrated care clinics compared to travel to hospital clinic. Methods Geriatric Medicine Integrated Care attendances from January 2023 to March 2023 were included. Distance from the patient’s home address to the location of Integrated Care Clinic (spoke), and to the affiliated hospital (hub) were recorded in kilometres. For the primary analysis we have not considered staff travel as there was heterogeneity in number of staff per clinic and distance travelled per staff member. Where more than one route was available, the shortest distance was recorded. Carbon emissions were estimated based on kilometres travelled by a standard new passenger car. Results Among 206 clinic attendances, the mean distance to ‘spoke’ clinic was 16.06 km (15.98). The mean distance to the ‘hub’ clinic was 39.29 km (17.82). The estimated total distance travelled by patients to integrated care clinics was 3293.45 km, whereas the distance which would have been travelled to ‘hub’ clinics was 8055.85 km. Total carbon emissions for 3 month travel to ‘spoke’ clinics were estimated at 371 kg, compared to 907 kg to the ‘hub’. Conclusion The development of integrated care services, which provides necessary care to older adults, may also have an environmental advantage. Healthcare’s contribution to climate change action is another incentive to invest in integrated care services.

Building Information Modeling Applications in Energy-Efficient Refurbishment of Existing Building Stock: A Case Study

The built environment contributes to 35% of the global energy consumption and 38% of energy-related carbon emissions. The exponential population growth, coupled with the inability of the existing building stock to meet demands or reach the end of its lifespan, has precipitated the proliferation of new constructions worldwide. However, it has been proven well that retrofitting existing buildings might impact the environment less, save resources, and reduce the carbon footprint while extending their lifecycle. Various techniques are available to assess the performance of existing buildings and quantify the energy-saving potential of renovation measures. Building information modeling (BIM) technology serves as a virtual laboratory for buildings and can be used to model building stocks and measure how building performance changes with alternative envelope and system proposals. This research study explores the potential of BIM-based energy modeling to evaluate the effectiveness of refurbishment scenarios on a residential building. A total of 192 alternative scenarios were developed by considering six variables (wall, roofing, insulation, glazing, lighting power density, and photovoltaic panels). The results were analyzed across annual energy consumption (fuel and electric), annual/lifecycle energy costs, energy use intensity, annual CO2 emissions, and initial investment costs. The optimum alternative scenario decreased the annual fuel and electricity consumption of the sample building by 61% and 64%, respectively. The payback period was calculated as 12 years. This study demonstrates the impact of BIM in enhancing the energy efficiency of the existing building stock, presenting results within the context of a residential building.

Prediction of energy-related carbon emission intensity in China, America, India, Russia, and Japan using a novel self-adaptive grey generalized Verhulst model

Excessive energy-related carbon emission intensity will lead to deterioration of environmental quality and hinder green economic growth. This paper proposes a novel self-adaptive fractional order grey generalized Verhulst model (SAFGGVM) to predict energy-related carbon emission intensity in China, America, India, Russia, and Japan with nonlinear and complex characteristics effectively. Firstly, self-adaptive fractional order and dynamic background value coefficient are introduced to capture the nonlinear evolutionary trends. Subsequently, the optimal nonlinear parameters are determined using Grey Wolf Optimization algorithm through comprehensive comparison. Furthermore, the flexibility of SAFGGVM is verified by presenting its relevance to existing models. Finally, the validity, applicability, and robustness of SAFGGVM are confirmed by comparing with two machine learning models and four grey prediction models. The empirical results exhibit that the overall precision of SAFGGVM model significantly prevails over the others in five cases, with MAPE values less than 6% in both the simulation and prediction intervals. The out-of-sample forecast results indicate that the energy-related carbon emission intensity of five countries will fall into 5.7024, 1.6030, 7.3668, 8.1633, 2.0399 tons/10,000 USD by 2025, and the decreasing rate of developing countries tends to catch up with, or even exceed, developed countries. Projection results can serve as a reference point for countries to achieve green and sustainable development.

A real-time operational carbon emission prediction method for the early design stage of residential units based on a convolutional neural network: A case study in Beijing, China

Buildings are currently responsible for 39% of global energy related carbon emissions. Thus, carbon reduction in the construction industry is crucial to global climate issues. This study contributes a real-time operational carbon emission prediction method for the early design stage of residential units based on a convolutional neural network (CNN). The CNN model was trained based on a dataset with 2000 real residential units focusing on typical cases in northern China, particularly in Beijing, which is a significant energy consumption area. The R2 values of the operational-stage heating and electricity carbon emissions in the validation dataset reached between 0.91 and 0.98. The trained model based on this method can provide the real-time prediction of the carbon emission potential of proposed residential units to buttress architects to make scientific design decisions. The model was then validated on new cases that varied in the total area, rooms, orientation, window locations, and window sizes. The performance of the CNN model is verified by comparing the results with the output of an EnergyPlus model. While the variation trends of the output results of the two methods are found to be consistent, the CNN model results still have certain deviations from those of the EnergyPlus model. Consequently, the proposed method can reflect the variation of operational-stage carbon emissions due to the change of the floor layouts of residential units, and has a faster speed and higher convenience than the simulation-based method.

Urbanization and Carbon Emission in South Asia

Background: Rapid urbanization in South Asia has led to increased energy consumption, transportation, and construction of new infrastructure, resulting in high carbon emissions. South Asia is the world's fastest urbanizing region, and its carbon emissions are influenced by the building industry, which accounts for more than 30% of the region's energy-related carbon emissions. This issue poses a serious policy concern as South Asia is vulnerable to the impact of carbon emissions and climate change. The relationship between urbanization and carbon emissions in South Asia is the concern of this paper. Objective: To analyze the relationship between urbanization and carbon emissions in South Asia and to test the validity of urbanization Kuznets' curve for the region. Method: Modified version of the Influence, Population, Affluence, and Technology (IPAT) model framework is used to study the relationship between carbon emissions and urbanization in South Asia. The data spans from 1970-2019. Results: The study finds a significant positive relationship between carbon emissions and urbanization, population, and population structure. The existence of Urbanization in Kuznets’ Curve for South Asia is proved. Implication: The positive relationship between carbon emissions and urbanization poses a major policy challenge in South Asia. With increasing population and economic growth, sustainable urbanization must be a top priority for policymakers in order to mitigate the effects of climate change.

Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model

To deal with global warming and fulfil China’s commitment to carbon neutrality by 2060, reducing carbon emissions has become a necessary requirement. As one of China’s three major economic circles, the Beijing–Tianjin–Hebei region (B–T–H) has a great responsibility. This paper measures energy-related carbon emissions of B–T–H from 2005 to 2019 and uses the extended Logarithmic Mean Division Index (LMDI) to decompose the carbon emission effect factors. Then, a Tapio index model was constructed to analyse the contribution of each effect factor. The results showed that: (1) the total carbon emissions of B–T–H increased by 1.5 times, with Hebei having the highest proportion, followed by Tianjin and Beijing. Coal was the biggest emitter in all three regions. Natural gas emissions in Tianjin and Beijing were growing rapidly. (2) Consistent with most studies, economic development promoted carbon emissions, while energy intensity and energy structure inhibited them. It was found that innovative factors also have significant impacts: research and development efficiency was the primary emission inhibition factor in Hebei and the secondary inhibition factor in Tianjin and Beijing. The effects of investment intensity and research and development intensity differed between regions. (3) Beijing took the lead in achieving strong decoupling, followed by Tianjin. Hebei maintained weak decoupling. Innovative factors also played an important role in decoupling, which cannot be ignored in achieving emission reduction targets.

Uncovering the coupling effect with energy-related carbon emissions and human development variety in Chinese provinces

Lessening energy-related carbon emissions has become a crucial measure to achieve Chinese carbon neutrality. This study is the first to construct a Difference in Carbon pressures-adjusted Human Development Index (DCHDI) model for the purpose of exploring the coupling effect between carbon emissions and human development variety from 2000 to 2019 in Chinese provinces. We demonstrate the following. (1) The total energy-related carbon footprint of 30 provinces in China reached 10.2 billion tons in 2019, with an average annual growth rate of 6.93% over the past two decades; and the provinces with the highest carbon emissions per capita are InnerMongolia, Ningxia, and Shanxi. (2) At the provincial level, we observed that the Human Development Index (HDI), which includes life expectancy, education, and income, has been rising, while Beijing, Shanghai, and Tianjin entered the super-high HDI level before 2008. (3) The entire coupling effect of 30 Chinese provinces has been broadly fortified in the last 20 years, but the growth rate of DCHDI values in 2011–2019 has slowed down compared with that in 2000–2010; the clustering phenomenon demonstrated that this discovery is associated with historical peaks in total carbon emissions. (4) The coordination degree of carbon emissions per capita and HDI was verified, and 96% of the data points were found in the range of super high coupling coordination degree. Overall, this study provides the government with worthwhile guidance for decision-making and carbon reduction strategies for other countries struggling to advance human sustainable development.

Study of the Carbon Neutral Path in China: A Literature Review

After the carbon peaking and carbon neutrality goals were proposed, different institutions and scholars carried out research on China’s medium- and long-term mitigation pathways. Through the literature review of China’s research on carbon neutrality, this paper finds that the zero-carbon energy transition is the key to achieving carbon neutrality. The main driving factors of reducing energy-related carbon emissions include the cleanliness of primary and secondary energy supply systems, the electrification of energy consumption and the development of hydrogen energy, energy efficiency improvement, carbon capture and storage (CCS), and negative emissions. Furthermore, based on the literature, this paper conducts a quantitative comparative analysis, and selects key indicators for comparative analysis and summary from seven dimensions, including economic and social development, carbon emission pathways, primary energy consumption, final energy consumption, final hydrogen energy consumption, electricity demand and supply, and installed capacity and structure of power generation. Main conclusions are as follows: (i) In terms of carbon emission pathways, the institutions generally believed that China will peak carbon emissions around 2028 and achieve carbon neutrality between 2050 and 2060. Achieving net-zero or near-zero emissions first in the power sector is the key to carbon neutrality across the society; (ii) In terms of energy supply, it is a consensus to increase the proportion of clean energy and reduce carbon emissions from the source. The proportion of clean energy in primary energy will increase to more than 85%, and the proportion of clean energy power generation and installed capacity will reach more than 90%; (iii) In terms of energy use, electricity will become the core of final energy consumption in the future. The predicted electricity consumption across the society will range 14.3–18.4[Formula: see text]PWh, and the predicted electrification rate will exceed 65%.

Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization

Being a primary contributor to global energy consumption and energy-related carbon emissions, the building and building construction sectors are a crucial player in the decarbonization and energy efficiency efforts. This article proposes a novel physically consistent deep learning (PCDL) approach for building thermal modeling and assesses its potential for optimizing building energy efficiency and indoor thermal comfort through model predictive control (MPC). The PCDL model considers physical relationships between system inputs and outputs and is applied to predict the indoor thermal climate. With the strict guarantee of satisfying the laws of physics, the proposed PCDL model has better generalization ability than other machine learning approaches. Subsequently, the PCDL model is integrated into an MPC controller to optimize building energy consumption and indoor thermal comfort. The proposed approach is tested on Carpenter Hall, a multi-zone office building located in Cornell University Campus, through simulations. Based on the simulation results, the PCDL model demonstrate a much better generalization ability for yielding physically-feasible predictions compared to the long short-term memory (LSTM) model. Compared to an On/Off controller, a state space model-based MPC controller, and an LSTM-based MPC controller, the proposed PCDL-based MPC reduces by 5.8%, 4.5%, and 8.9% energy consumption and improves by 55%, 59%, and 64% indoor thermal comfort, respectively, and therefore enhances the building decarbonization progress. The results emphasize the importance of considering physics information in data-driven models and highlight the advantages of the proposed PCDL-based MPC controller.

Sustainable Planning Strategy of Dairy Farming in China Based on Carbon Emission from Direct Energy Consumption

The mechanical and electrical development in dairy farming in China increases energy-related carbon emission (CE). To support the sustainable planning strategy of the department, this study calculated the CE and the carbon emission intensity (CI) of the direct energy consumed in dairy farms from 21 provinces in China. Through four dimensions analysis including the national level, farm scale, inter-provincial distribution, and main producing area, this study illustrates the impact of the environment, production, and management on CE. The total CE of nationwide dairy farming was about 2.4 Tg CO2 eq. in 2019, and the CIs of the 21 provinces varied from 0.009 to 0.216 kg CO2 eq. per kg of milk. The results indicate that the management mode applied in large-scale dairy farms (500 heads and above) varies considerably due to inadequate adaptation to climate. In general, semi-arid and semi-humid regions are more suitable for dairy farming than arid and humid regions. In the main milk-producing area, the spatial aggregation effect is visible in the carbon reduction potential. The present study suggests that further steps to promote sustainability and milk productivity are embodied when the replacement of fossil fuels and the management standardization are adapted to regional characteristics.

Time Series Forecast of Cooling Demand for Sustainable Chiller System in an Office Building in a Subtropical Climate

Commercial buildings can take up one-third of the energy related carbon emissions. There is limited research on forecasting cooling demands to evaluate sustainable air conditioning systems under climate change. This paper develops a simplified cooling demand model based on the time series of climatic and architectural variables to analyze carbon reduction by a sustainable chiller system. EnergyPlus is used to simulate hourly cooling demands of a hypothesized high-rise office building in Hong Kong under a change of architectural parameters and future climate conditions. An hourly cooling demand model with R2 above 0.9 is developed with inputs of the window-to-wall ratio, outdoor air enthalpy, global solar radiation, wind speed and their two steps ahead. The validated model is then used to analyze carbon reduction potentials by free cooling and a full variable speed chiller system. The low carbon technologies reduce carbon emissions by over 20% with but the reduction shrinks to 2.51–4.93% under future climate conditions. The novelty of this study is the simplified cooling demand model based on the time series of climatic and architectural variables. The significances of this study are to quantify carbon reduction by a sustainable chiller system under climate change and to appeal for more carbon reduction technologies for carbon neutrality.

The static and dynamic carbon emission efficiency of transport industry in China

Transport industry is one of the major contributors to energy-related carbon emissions in China, thus becoming a topical area of concern where efforts to improve carbon emission efficiency need to be prioritized. By establishing a Static-Dynamic Efficiency (SDE) framework and using panel data from 30 provinces in China from 2010 to 2019, we develop a super slack-based measure model to assess the static efficiencies and potential for improvement of the TICEE at the provincial level, adopt the global Malmquist-Luenberger index to examine the dynamic changes in TICEE and the underlying driving factors, and propose improvement strategies by integrating the static and dynamic perspectives. We observe: (1) severe carbon emission inefficiency in transport industry in China, particularly in western, central and north-eastern regions; (2) a slow downward trend in static efficiency due to the slow progressing technological innovation; and (3) high potential for saving energy and reducing emissions in the provinces that fall into the “inefficient-deterioration” category. Our research findings extend the literature by bringing together the static and dynamic aspects into the SDE framework, as well as assist policy makers in better understanding the way to improve TICEE.