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In response to global warming, China has formulated the "double carbon" strategic goals of peaking carbon dioxide emissions before 2030 and reaching carbon neutrality before 2060. The problem of rural carbon emissions is often ignored due to underdeveloped industries and services. In this paper, the carbon emission pathways in the rural areas of Guangdong Province are investigated. Since energy consumption is the main source of carbon emissions, the factor analysis was used to analyze the main factors affecting rural household energy consumption and agricultural production energy consumption. Multiple linear regression was conducted to predict the rural energy consumption demand in Guangdong. Furthermore, the current situation and development trend of rural energy supply, demand and consumption structure, and the potential of renewable energy development were considered to predict carbon emissions in the rural areas of Guangdong. Moreover, the carbon emission pathways in the rural areas of Guangdong were discussed under two scenarios: the base scenario and the radical model. [ABSTRACT FROM AUTHOR]

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China's high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption. [ABSTRACT FROM AUTHOR]

Background: With the purpose of exploring the development of new quality productive forces in Guangdong, the present study hypothesized that reducing energy and carbon inputs was beneficial for increasing Guangdong's multi-cropping agricultural energy output and economic returns. Methods: The energy use efficiency of crop production and the corresponding carbon input and agricultural benefit trends from 2011 to 2021 were examined by linear regression analysis for Guangdong Province, China. The corresponding development trends were also predicted using a grey model. Results: The results indicated that the total energy output increased by 12.50%, wherein the energy productivity levels of peanuts, vegetables, mulberry, and fruits increased greatly (51.27–106.17%), while the total energy input and the total carbon input decreased by 12.17% and 6.37%, respectively. Moreover, the energy input per carbon input decreased by 6.19%, while the energy output per carbon input increased by 20.15%. Both energy-related indicators and economic-related indicators all had substantially increased (28.08–44.97% and 83.86–120.91%, respectively). Grey model predictions show that the agricultural output value increased steadily under the current agricultural policy of reducing fossil energy input. Conclusions: The current low-carbon and high-output agricultural model is beneficial for increasing Guangdong's multi-cropping agricultural economic returns and mitigating greenhouse effects. [ABSTRACT FROM AUTHOR]

Guangdong is China's largest province in terms of energy consumption. The energy-related carbon emissions in Guangdong province are calculated, and two extended and improved decomposition models for energy-related carbon emissions are established with the Logarithmic Mean Divisia Index method based on the basic principle of Kaya identity. Main results are as follows: (1) the energy-related carbon emissions from the three strata of industry, except the primary industry, and household energy consumption in Guangdong province show increasing trend from 1995 to 2009; (2) the main driving and inhibiting factors which influence energy-related carbon emissions are economic output and energy intensity, respectively, while the contributions of energy mix, industrial structures, population size and living standards are not significant during the period of interest. It is concluded that optimizing the energy mix by exploiting new energy sources and cutting down energy intensity by developing low-carbon technologies are the two most effective approaches to reduce carbon emissions for Guangdong province in the future. The results and proposals in this paper provided reference for relevant administrative departments in the Government of Guangdong province to develop policies for energy conservation and emission reduction as well as to promote development of low-carbon economy. [ABSTRACT FROM AUTHOR]

Abstract: Making new coal-fired power plants carbon capture ready (Carbon Capture Ready) in China has been recognised as a crucial by a number of stakeholders academics, energy companies and regional government, based on a study in EU-UK-China NZEC project. A number of publications have investigated the definition, engineering requirements, economic and finance of CCR for China. However there remain a number of questions regarding the extent to which a plant’s physical location might constrain the feasibility of CCS retrofit. To address this issue, a Geographical Information System (GIS) has been used as a tool for mapping current and planned large carbon dioxide sources in Guangdong, also illustrating potential storage sites and calculating possible carbon dioxide transportation route. This paper investigates the location factors that should be considered when locating new build CCR power plants and demonstrates the methodology of using GIS software with spatial analysis in planning new build power plant in Guangdong. A preliminary study has identified over 30 large power plants within the region, with plant locations and historical emission data collected and presented in ArcGIS. Factors such as distance to potential storage site, route of CO2 pipeline, extra space on site and potential development plan etc. were investigated in the modelling and calculated the potential source and sink solution. The study then moves on to suggest possible new build plant locations which can be easily fitted in to the current network, based on economic optimisation. The scope for future coal plant development combined with a possible nuclear plant siting plan is discussed towards the end of the paper. Guangdong province, which owns the third largest coal-fired power installed capacity out of 31 provinces, generated over 8% of China’s total electricity every year for the past 15 years. CO2 storage opportunities could be found in the surrounding South China Sea, where Guangdong has a total of 4,300 km of coastline and some small scale oil fields on shore within the region. It is also among the first places to start the national open and reform policy in China. The province is one of the richest in China, with the highest GDP among all other provinces since 1989, and the foreign trade accounts for more than a quarter of China’s total amount. It also contributes around 12 of the total national economic output. Currently, the provincial government is proposing a low carbon roadmap, which is the first of its kind in China. The work has created a totally new thinking on capture ready power plant planning. This differs from existing studies (e.g., which aim to investigate the existing carbon dioxide emission sources at specified location and provide source and sink matching analysis. Instead the study focuses on policy implementation for new build capture ready power plants. Three clusters within Guangdong province are identified as potential temporary CO2 storage hubs before transporting the gas to a long term storage site. When officials are planning new power plant locations from a capture ready perspective, the plants should not necessarily be close to storage sites in straight line, but rather should be within a reasonable distance of a cluster. Transport of the captured CO2 will not be limited to pipelines, but could be extended to road and rail tankers. Power plant parameters and storage site data were collected for this research. Public transportation, utilities, landscapes, river, land used and population data were referenced from various sources; therefore, some of the data could be out of date. Nevertheless, it should still provide enough information when deciding the location of the transport cluster. Any future work could build on the existing model with updated data. Moreover, it could fit in with the national natural gas transportation network and utility planning network to provide long term integrated energy system analysis. The paper could provide policy makers, investors and urban planning officials with a view on how conventional thermal power plant investment and planning could be optimised, using Carbon Capture Ready designs, to keep the CCS retrofitting option open. [Copyright &y& Elsevier]

Analyzing the influencing factors of PM2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM2.5 dataset and economic and social statistics for Guangdong Province from 2010 to 2019 were selected, and a PM2.5 pollution management compliance path formulation method based on the multi-scenario simulation was proposed by combining the differences in city types and PM2.5 concentration prediction. Based on the prediction model of PM2.5 concentration constructed by the Ridge and SVM models and facing the PM2.5 pollution control target in 2025, the urban PM2.5 pollution control scenario considering the characteristics of urban development was constructed. According to the scenario simulation results of the PM2.5 prediction model, the PM2.5 pollution control path suitable for Guangdong Province during the 14th Five-Year Plan period was explored. The coupling coordination model was used to explore the spatial and temporal pattern evolution of PM2.5 pollution collaborative governance in various prefecture-level cities under the standard path, and the policy recommendations for PM2.5 pollution control during the 14th Five-Year Plan period are proposed. The results showed the following: ① in the case of small samples, the model can provide effective simulation predictions for the study of urban pollutant management compliance pathways. ② Under the scenario of PM2.5 management meeting the standard, in 2025, the annual average mass concentration of PM2.5 in all prefecture-level cities in Guangdong Province will be lower than 22 μg/m3, and the annual average concentration of PM2.5 in the whole province will drop from 25.91 μg/m3 to 21.04 μg/m3, which will fulfil the goal of reducing the annual average concentration of PM2.5 in the whole province to below 22 μg/m3, as set out in the 14th Five-Year Plan for the Ecological Environmental Protection of Guangdong Province. ③ Under the path of PM2.5 control and attainment, the regional coordination relationship among prefecture-level cities in Guangdong Province is gradually optimized, the number of intermediate-level coordinated cities will increase, and the overall spatial distribution pattern will be low in the middle and high in the surrounding area. Based on the characteristics of the four city types, it is recommended that a staggered development strategy be implemented to achieve synergy between economic development and environmental quality. Urban type I should focus on restructuring freight transportation to reduce urban pollutant emissions. City type II should focus on urban transportation and greening. For city type III, the focus should be on optimizing the industrial structure, adjusting the freight structure, and increasing the greening rate of the city. For city type IV, industrial upgrading, energy efficiency, freight structure, and management of industrial pollutant emissions should be strengthened. [ABSTRACT FROM AUTHOR]

Experiences worldwide indicate that the relationship between energy consumption and economic development is complex. Guangdong Province has experienced a more than threefold increase in GDP since 2005, but it is not clear whether rapid economic growth is at the cost of a large amount of energy consumption. In this study, we employ spatial autocorrelation and hotspot analysis to discover the spatial agglomeration of GDP per capita and energy intensity in Guangdong, China, from 2005 to 2018. Furthermore, panel vector autoregression coupled with a system generalized method of moments is performed to examine the dynamic causal relationship between energy consumption and economic growth under the framework of the Cobb–Douglas production function. We used a multivariate model and grouped studies based on the differences in regional economic development and the results with similar research. The results show that the GDP per capita of the Pearl River Delta (PRD) is significantly higher than that of the peripheral municipalities. However, energy intensity shows an entirely different spatial distribution. The development of the regional economy depends on its own "assembling effect". In regions with significant differences in economic development, it is necessary to conduct grouping studies; otherwise, the overall results will not represent partial results. GDP explains approximately 68.3% of the total variation in energy consumption in the PRD but only approximately 34.5% of that in the peripheral municipalities. We do not confirm Granger causality between energy consumption and economic development. Guangdong can decrease its energy consumption growth without substantially sacrificing its economic growth. The contribution of this study is to propose an analytical framework of the relationship between energy consumption and economic development that integrates time and space perspectives to account for spatial effects in panel analysis. [ABSTRACT FROM AUTHOR]

Social progress and economic development has resulted in the need to focus on the impacts of building microenvironment on the energy consumption in office buildings. The concept of a building's microenvironment was introduced to understand the local microclimate around a building that is formed by the surrounding urban green spaces, the distribution of roads, and building proximity. For this research, we adopted a regression analysis to quantify the impacts of building microenvironment on energy consumption in office buildings. Taking the government office buildings of Guangdong Province as an example, we measured the building microenvironment through the urban green space density, road density, and number of points of interest (POI) around the buildings. The results showed that when the green space density increased by one unit, the energy consumption in government office buildings was reduced by 0.277%. Moreover, an increase of 1% in road density and in the number of POI increased the energy consumption in government office buildings by 0.288% and 0.048%, respectively. Furthermore, we discussed the heterogeneous impacts of building microenvironment on the energy consumption in government office buildings at varying scale levels. Green space and road density had less impact on the energy consumption in larger buildings, whereas the number of POI had no significant impact on small-scale buildings but did have a significant impact on large-scale buildings. There were also some limitations in the study. The data were limited to government office buildings, and did not include panel data, as well as it lacked building characteristics such as orientation, floor height, and building materials. In addition, it was impossible to evaluate the impacts of meteorological factors such as wind speed and thermal radiation on energy consumption in buildings. Nonetheless, our study demonstrates that energy-aware urban planning and design have the potential to unlock energy efficiency for cities worldwide. [ABSTRACT FROM AUTHOR]

Abstract: As an important part of economical society, colleges and universities have been shouldering the important task of education, science research and social service. In this paper, a detailed investigation in the form of questionnaire was carried out for the energy consumption of colleges and universities in Guangdong Province, including electricity, water, gas and cooling energy consumption from 2006 to 2010. The energy meter reading modes, energy conversation investment amount and energy-efficiency retrofit work were also reported and analyzed. Furthermore, as can be concluded from the survey, there exists great difference in per unit energy consumption between different types of universities classified by schools’ discipline, nature and level, which would be taken into account when the energy consumption evaluation indexes system is established. Although energy-saving measures at Guangdong's colleges and universities bear fruit, Guangdong's colleges and universities still have great potential of energy conservation and emission reduction by Conservation-oriented campus construction, and the government should increase funding and improve economic policy to encourage and support energy saving for colleges and universities. [Copyright &y& Elsevier]

• The new power supply structure resulting from the new policy has been considered. • The new models project the inefficient points onto a more realistic efficient frontier. • The new models have the power to allocate the shared resources. • Results show that the power production stage is allocated less shared resources. • The power sale stage's efficiency is lower than the one of power production stage. To relieve the problems of energy shortage and environmental pollution, many countries have implemented a variety of policies to improve the energy efficiency of the power industry. To understand the impacts of such policies on energy efficiency, this paper constructs novel network data envelopment analysis models to recognize the policy-induced structural changes in the power system. Compared with previous studies, the new models project the inefficient points onto a more realistic efficient production frontier, considering how the distribution proportion of shared resources will change when the power system, modeled as a power generation stage and a power sale stage, is led by the latter stage. Our empirical study on China's power system shows that the province of Guangdong and the eastern region of China perform best, and all power production stages perform better than the corresponding power sale stages. The development of the two power subsystems in the four regions is unbalanced. More than half of the power supply systems choose to allocate more proportion of the shared resources to the sale stage rather than use a more balanced distribution between the two stages. [ABSTRACT FROM AUTHOR]

Abstract: To find the key impact factors of CO2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO2 emissions. [Copyright &y& Elsevier]

Abstract: This paper uses two methods LMDI and Laspeyres to analysis the influences the industrial structure has on energy consumption,including how much the three industries have contribution to energy intensity .The results we conclude are:1) Economy has a strong stimulating effect on energy consumption;2)The effect of improving energy efficiency by using science and technology is significant in Guangdong province, especially in the secondary industry; 3) The secondary industry plays a positive role in reducing energy intensity, and the advantages of the tertiary industry isn’t displayed. [Copyright &y& Elsevier]

Energy consumption prediction is a hot issue, which is of great significance to regional energy security. In the existing prediction research with small samples, the time delay characteristic of an energy consumption system in itself is often ignored. To reflect the time delay characteristic of an energy consumption system and accurately grasp its development trend, a novel nonlinear time delay grey model with fractional order accumulation is presented. The new model is utilized to forecast and analyze Guangdong's primary energy consumption, in which the time delay parameter is ascertained by the grey correlation analysis method, and the other parameters are determined via particle swarm optimization. The results show the simulation accuracy of the new model is higher than those of the other 3 grey models, and the predicted results in the next three years can provide decision-making and theoretical reference for the relevant departments of Guangdong province. [ABSTRACT FROM AUTHOR]

Because of irreversibility on building construction, building energy efficiency design is more depended on simulation technology. Ministry of Housing and Urban-Rural Development of China also stated that China's building energy consumption accounted for 27.5% of the total energy consumption in 2012. Energy consumption is simulated based on the heat transfer principle of building wall, windows, roof and ventilation. Improved measurements are proposed for simulation cases. The heating, ventilation, and air conditioning energy consumption of benchmark and energy efficiency building are simulated based on EnergyPlus software. The most effective energy-saving measurements of energy efficiency building are improving air-conditioning system performance and thermal properties of wall and window. The results show that the energy efficiency ratio of refrigeration system should be more than three and energy-saving ratio is about 30%. Heat transfer coefficient of wall and window should be less than 1.0 W/m2k- and 2.0, W/m2k- the energy-saving ratio is more than 16% and 10%, respectively. The sum energy-saving ratios of refrigeration system, wall and window are about 56%. The energy efficiency ratios of roof and air exchanges number are not very obvious. Some energy-saving technologies with high cost are put forward based on simulation results which provide effective ways for building energy efficiency in Guangdong province, China. [ABSTRACT FROM AUTHOR]

The article applies a three-stage Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) pattern to examine the relationship between energy consumption and unfavorable CO2 emissions on green sustainable development, for the 11 cities of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2010–2016, by going through various violated factors and stochastic disturbance. Labor, capital and energy resource are chosen as input variables, while GDP and CO2 emission as output variables. During the three phases consisting of the SBM-DEA model (first stage and third stage) and SFA analysis (second stage), CO2 emission is considered as an unfavorable outcome, while stochastic statistical disturbances and external environmental influences are identified. The results show that the average efficiency of the GBA cities is 0.708, with only Shenzhen, Macao SAR and Hong Kong SAR having an efficiency of 1 during the whole study period. Based on the findings, suggestions are made for the GBA cities' sustainable development aspects. [ABSTRACT FROM AUTHOR]

Electricity transmission in a grid-connected system provides an effective solution to promoting clean energy accommodation. However, with arbitrary determination in current operation models, the clean energy utilization ratio (CEUR) is not satisfactory largely due to the lack of electricity curtailment (the electricity equivalent of clean energy curtailment) cost-dependent optimization. In this study, a curtailment cost-dependent multi-objective operation (CCMO) model was proposed to complementarily operate a grid-connected hybrid energy system, identify optimal CEUR, and thus maximally reduce electricity curtailment. The CCMO model centers on coupling the punishment cost of electricity curtailment with the multi-objective function defined as the total cost of each grid component. The CCMO model was solved to derive the optimal equilibrium solution determined based on multiple non-dominated solutions. A grid-connected hybrid energy system including the Yunnan, Guangdong, and Guangxi Power Grids was used to test the model performance. The results showed that the CCMO model's CEUR was up to 100% at hourly scale and 96.9% on daily average, which were both significantly higher than those in the current operation models. Furthermore, the CCMO's optimal equilibrium solution, i.e., respective minimum total cost of each grid component, can also identify optimal transmission schemes of the daily channel utilization to make the peak utilization hours largest. [ABSTRACT FROM AUTHOR]