Electricity Consumption Intensity Research Articles

Carbon Footprint Drivers in China’s Municipal Wastewater Treatment Plants and Mitigation Opportunities through Electricity and Chemical Efficiency

Reducing greenhouse gas (GHG) emissions to address climate change is a global consensus, and municipal wastewater treatment plants (MWWTPs) should lead the way in low-carbon sustainable development. However, achieving effluent discharge standards often requires considerable energy and chemical consumption during operation, resulting in significant carbon footprints. In this study, GHG emissions are systematically accounted for, and the driving factors of carbon footprint growth in China’s MWWTPs are explored. In 2020, a total of 41.9 million tons (Mt) of carbon dioxide equivalent (CO2-eq) were released by the sector, with nearly two-thirds being indirect emissions resulting from energy and material usage. The intensity of electricity, carbon source, and phosphorus removing agent consumption increasingly influence carbon footprint growth over time. Through statistical inference, benchmarks for electricity and chemical consumption intensity are established across all MWWTPs under various operational conditions, and the potential for mitigation through more efficient energy and material utilization is calculated. The results suggest that many MWWTPs offer significant opportunities for emission reduction. Consequently, empirical decarbonization measures, including intelligent device control, optimization of aeration equipment, energy recovery initiatives, and other enhancements to improve operational and carbon efficiency, are recommended.

Modeling Indirect Greenhouse Gas Emissions Sources from Urban Wastewater Treatment Plants: Integrating Machine Learning Models to Compensate for Sparse Parameters with Abundant Observations.

Electricity consumption and sludge yield (SY) are important indirect greenhouse gas (GHG) emission sources in wastewater treatment plants (WWTPs). Predicting these byproducts is crucial for tailoring technology-related policy decisions. However, it challenges balancing mass balance models and mechanistic models that respectively have limited intervariable nexus representation and excessive requirements on operational parameters. Herein, we propose integrating two machine learning models, namely, gradient boosting tree (GBT) and deep learning (DL), to precisely pointwise model electricity consumption intensity (ECI) and SY for WWTPs in China. Results indicate that GBT and DL are capable of mining massive data to compensate for the lack of available parameters, providing a comprehensive modeling focusing on operation conditions and designed parameters, respectively. The proposed model reveals that lower ECI and SY were associated with higher treated wastewater volumes, more lenient effluent standards, and newer equipment. Moreover, ECI and SY showed different patterns when influent biochemical oxygen demand is above or below 100 mg/L in the anaerobic-anoxic-oxic process. Therefore, managing ECI and SY requires quantifying the coupling relationships between biochemical reactions instead of isolating each variable. Furthermore, the proposed models demonstrate potential economic-related inequalities resulting from synergizing water pollution and GHG emissions management.

The allometric growth relationship between electricity consumption and economics in China.

Electric power is the basic industry of a modern country. The rapid development of the power industry has promoted economic development and social progress. With the establishment of the carbon neutrality target of carbon peak, China's power industry is also facing new situations and new challenges. This paper innovatively introduces the concept of allometric growth in biology, and uses the data of provincial electricity consumption and economic development from 1972 to 2017, to study the allometric growth relationship between electricity consumption and economics in China at multiple scales. Combined with the spatial autocorrelation analysis method, the temporal and spatial characteristics of allometric growth between electricity consumption and economic development were researched based on the analysis of spatial correlation of GDP electricity consumption intensity. The results show that: (1) The provincial GDP electricity consumption intensity shows an aggregate distribution, and the local aggregate pattern changed significantly from 1972 to 2017. (2) The High-High Cluster gradually disappeared, and the Low-Low Cluster moved southward, appearing in the middle and lower reaches of the Yangtze River. (3) The differential growth coefficient of electricity consumption and economic development increased initially and decreased from 1972 to 2017. (4) GDP would increase by 7.4%, 20.1%, and 15.6% in the simulation of electricity consumption, increasing 10% at the three stages of China's economic development. (5) The allometric growth coefficients of electricity consumption and economic development are very different in the three economic belts, eight economic zones, and provinces with different characteristics at the different stages of economic development. Formulating differentiated regulation policies for regional economic development and electricity consumption will be conducive to the coordinated development of electric power production and the national economy.

Study on the spatial correlation effects and influencing factors of carbon emissions from the electricity industry: a fresh evidence from China.

Carbon emissions from the electricity industry (CEEI) account for a large proportion of China's total carbon emissions, and it is important to study the spatial correlation between CEEI and the influencing factors to promote cross-regional synergistic emission reduction and low-carbon development of the power system. In this paper, the quasi-input-output (QIO) model is applied to assess the transfer of carbon emissions generated by electricity trading based on the consideration of electricity carbon transfer, and the exploratory spatial data analysis (ESDA) method is applied to analyze the spatial correlation effect of carbon emissions from China's electric power sector from 2001 to 2020, analyzes its distribution pattern in both spatial and temporal dimensions, and applies the improved logarithmic mean Divisia index (LMDI) two-stage decomposition model to decompose the changes in CEEI into 11 influencing factors from the perspective of the whole industrial chain of power production, transmission, trade, and consumption. The research results show that (1) the spatial distribution of CEEI has obvious unevenness and aggregation characteristics, with high-high aggregation areas and hot spot aggregation areas generally concentrated in the North China Power Grid and the East China Power Grid, but the aggregation trend is gradually decreasing, while low-low aggregation areas and cold spot aggregation areas are concentrated in the Northwest China Power Grid and the Central China Power Grid, but the area is very limited. (2) The direction of carbon emission diffusion in China's electricity industry is gradually transitioning from southwest-northeast to northwest-southeast, and the east-west diffusion trend is stronger than the north-south diffusion trend and carbon emissions are gradually shifting to the northwest grid. (3) The total amount of electricity production is the most influential factor in the change of CEEI, driving the cumulative growth of CEEI by 4495.34 Mt, followed by GDP per capita and electricity consumption intensity. Coal consumption for power generation, the share of thermal power, and net electricity exports were the main factors inhibiting the increase in carbon emissions from the power sector, with cumulative contributions of -797.74 Mt, -619.99 Mt, and -47.76 Mt, respectively.

Analysis of the Influencing Factors of Power Demand in Beijing Based on the LMDI Model

Since the reform and opening-up, under the new economic situation and policy, the rapid growth of power demand in Beijing is threatening the sustainable development of China’s economy and environment. To recognize the driving factors of electricity consumption growth and offer policy implications, based on the data of electricity consumption, the Gross Domestic Product (GDP) and the resident population in Beijing from 1990 to 2021, this research used the Kaya-equation and logarithmic mean divisia index (LMDI) model to decompose the growth of power demand in Beijing into the quantitative contribution of each driving factor from the perspective of industrial electricity consumption and residential electricity consumption. The results of the decomposition analysis show that, as far as industrial electricity consumption is concerned, the contribution rates of economic growth, electricity consumption intensity and output value structure to industrial electricity growth are 234.26%, −109.01% and −25.25%, respectively, which shows that economic growth is the primary driving force promoting the growth of industrial electricity demand. Power consumption intensity is the main reason for restraining the growth of industrial power demand, the growth rate is sliding and the contribution of the industrial structure is relatively small; as far as residential power consumption is concerned, the contribution rates of per capita power consumption and population size to residential power growth are 68.13% and 31.87%, respectively, which indicates that per capita power consumption is the main factor promoting the growth of residential power demand, followed by the total population. The study results show that the consumption of electric power would increase if Beijing’s economy and urbanization keep developing, and optimizing the industry structure, improving the efficiency of electric energy utilization and adopting clean power energy are the main approaches to making Beijing’s consumption of electric power decrease.

Does China's low-carbon city pilot intervention limit electricity consumption? An analysis of industrial energy efficiency using time-varying DID model

Emissions depletion through pro-environmental interventions by top emitters like China has profound significance in improving the atmosphere's cumulative carbon efficiency. China's electricity sector is accountable for almost 40% of its total aggregated emissions, indicating that China's electricity sector does not yet follow the energy conservation and energy efficiency theories, which require urgent attention if China aspires to achieve carbon emissions peak and neutrality by 2030 and 2050, respectively. Chinese lawmakers have adopted pro-environmental interventions to limit emissions, like constructing low-carbon city pilots (LCCP). LCCP intervention has successfully curbed emissions in China's major cities, which earlier studies have validated. However, no study assesses whether the LCCP intervention supports enterprises in reducing their electric energy consumption intensity (EECI) by upgrading their energy conservation attitude and improving power efficiency. To address this fundamental question, we construct a time-varying difference-in-difference (DID) model to comprehensively investigate the consequences of LCCP intervention on EECI, using matched data from the Chinese Enterprise Pollution Emissions Database and the Chinese Industrial Enterprise Database from 2007 to 2014. The findings unfold as follows: 1) The LCCP intervention significantly reduces the firm's electricity consumption intensity (i.e., a one percentage point increase in LCCP curbs EECI by 3.14%). 2) We observe significant heterogeneity in the effect of LCCP on EECI based on the ownership, location, and input characteristics of the intervened firms (i.e., LCCP has an insignificant effect on EECI for firms that are state-owned, labor-driven, and located in western zones. However, it has a more substantial inhibitory effect on EECI for firms that are: located in non-resource-based cities, foreign and private-owned, capital and technology-driven, and non-western). 3) The LCCP intervention promotes enterprises' power consumption via the energy-saving and green innovation effects, which boosts enterprises' energy conservation attitude and energy efficiency in the long term. The conclusion remains untapped after mitigating sequence-related issues, limiting the influence of alternative policies, employing lagged terms, and deploying different estimation strategies (i.e., PSM-DID). Several policies have been advocated in line with the findings in the ultimate section.

Benchmarking of Energy Consumption in Higher Education Buildings in Saudi Arabia to Be Sustainable: Sulaiman Al-Rajhi University Case

Electricity is used in educational buildings, and there are now numerous attempts to reduce consumption, achieve sustainability, and protect the environment. This paper aims to study energy consumption, identify opportunities to rationalize energy consumption, and propose solutions at Sulaiman Al-Rajhi University in the Kingdom of Saudi Arabia in order for the university campus to become sustainable. The results showed that total annual electrical consumption totaled 13,859 MWh in 2021. The air conditioning system, other devices, and lighting have the percentage of consumption 79, 14, and 7% of the total, respectively. Electricity consumption intensity was approximately 145–155 kWh/m2, and the per capita intensity was approximately 12,987–16,351 kWh, in the period 2017–2021. The price of the electrical energy generated on the university campus ranged between 0.3 and 0.53 SR/kWh in 2021, while the electricity tariff from the grid for educational buildings was 0.18 SR/kWh. This means that the public grid was 50% cheaper than campus-generated electricity. At the price of energy generated by generators, the total simple payback time (SPBT) for the suggested energy conservation measures (ECMs) is 4.13 years, whereas the SPBT is 8.96 in the case of the consumption of the electricity supplied by the national grid. The environmental benefits of the proposed ECMs were also identified.

Greenhouse gas emissions from wastewater treatment plants in China: Historical emissions and future mitigation potentials

Accurate estimation of greenhouse gas (GHG) emissions characteristics and future mitigation potentials of China's wastewater treatment plants (WWTPs) is essential to propose suitable mitigation strategies. Here we employ the population-equivalent method recommended by the IPCC to analyze GHG emission characteristics and hotspots of China's WWTPs from 2005 to 2020 and assess the mitigation potentials by 2035. Results show that GHG emissions from China's WWTPs more than tripled from 13.34 Mt CO2-eq in 2005 to 30.95 Mt CO2-eq in 2020. Due to the differences in electricity consumption intensity, emission factors, and economic development level, there is significant spatial heterogeneity in the amount, structure, and intensity of emissions by province. Scenario analysis reveals that energy-saving improvement generates the largest mitigation potential (15%), followed by operational optimization (10%) and thermal energy recovery (10%), while chemical energy recovery (4%) and solar energy utilization (3%) contribute the least. Taking all mitigation measures, emissions can reduce by about 41% to 21.14 Mt CO2-eq in 2035. What's more, the mitigation effect of different decarbonization measures varies among provinces. Our results highlight the need for targeted policy in priority areas and region-specific strategies.

Spatio-temporal analysis of decoupling and spatial clustering decomposition of CO2 emissions in 335 Chinese cities

• Spatio-temporal analysis of the decoupling of urban development levels and CO 2 emissions. • Spatial decomposition of CO 2 emissions for 335 cities. • Chinese cities can be clustered into nine types according to their development status and resource endowment. • The critical intra-group decomposition factors differed for the nine types of cities. • Population intensity is the most critical factor contributing to the inter-group differences in urban CO 2 emissions. The decoupling of urban development levels (UDL) and CO 2 emissions is an essential representation of sustainable urban development. The spatial decomposition of urban CO 2 emissions is an essential reference for achieving differential CO 2 emission reduction. This study first evaluates the UDL of 335 cities from 2009 to 2019 using the entropy weight-TOPSIS method. The Tapio decoupling method is used to analyze the spatio-temporal evolution of the decoupling relationship between UDL and CO 2 emissions. Second, the Logarithmic Mean Divisia Index (LMDI) method is used to spatially decompose urban CO 2 emissions and explore their key influencing factors. Finally, cities with the same development status and resource endowment are spatially clustered by density peaks. Moreover, the proposed spatial LMDI method is used for intra-group and inter-group decomposition of CO 2 emissions. The results show that the decoupling status can be divided into three stages. CO 2 emission intensity and electricity consumption intensity show mainly north–south differences, while other factors show mainly east–west differences. Cities can be classified into nine types by clustering, and the critical intra-group decomposition factors are different. Population intensity is the most critical factor contributing to the inter-group differences in urban CO 2 emissions, followed by land size and output intensity.

Carbon emission from the electric power industry in Jiangsu province, China: Historical evolution and future prediction

This paper takes Jiangsu as an example to measure the carbon emissions from the electric power industry from 2002 to 2017, builds an extended STIRPAT model to quantify its driving factors, and uses the Monte Carlo method to simulate the evolution of carbon emissions in multiple scenarios from 2018 to 2030. The results show that: (1)Population scale, urbanization level, GDP per capita, industrial added value, and electricity consumption intensity promote the increase of carbon emissions in the electric power industry. (2)Trade openness and the transmission level of other provinces play a role in reducing carbon emissions. (3)Under the baseline scenario and the green development scenario, the carbon emissions of the electric power industry have shown a continuous growth trend, but the growth rate of carbon emissions has slowed down significantly under the green development scenario.

Electricity consumption variation of public buildings in response to COVID-19 restriction and easing policies: A case study in Scotland, U.K.

A growing number of studies have showed energy demand changes during COVID-19; this study aims to further disclose the impact of the restriction and easing policies on the energy consumption of public buildings where occupants’ usage and activities are regulated in response to the pandemic. This study analyzes half-hourly electricity consumption data of 35 public buildings covering 6 building types in the Perth and Kinross Council area in Scotland, U.K., over the span of 2020 and 2021. The results show that the restriction has a greater impact on the electricity reduction in the first year of the pandemic than that in the second year. In response to the restriction, the electricity use intensity of all public buildings reduces significantly (p < 0.001) except office buildings with no significant reduction (p > 0.05); secondary schools have the highest electricity consumption reduction (275.04 kwh/day), while museums have the lowest reduction (58.62 kwh/day). In addition, the electricity consumption and electricity use intensity of museum, library and school buildings are inversely proportional to the restriction intensity, while this is opposite for office buildings. Combing restriction intensity and mobility data, this research reveals the different impacts of the restriction policies on the electricity consumption of public buildings during the pandemic, which reflects people’s changing attitudes and behaviors towards COVID-19. The results provide a reference basis for energy management to develop more realistic energy demand policies based on public building types and to optimize the electricity supply load and energy profile during the COVID-19 pandemic.

Spatiotemporal Evolution and Influencing Factors of Electricity Consumption in the Yangtze River Delta Region

Electricity consumption accounts for a considerable part of the final energy consumption, and it is important for economic development and human life. This study explores the spatiotemporal evolution characteristics and influencing factors of electricity consumption in the Yangtze River Delta region in China from 2006 to 2019, using the gravity model and Logarithmic Mean Divisia Index method, respectively. The results show that: (1) The centers of gravity for the total final, industrial and residential electricity consumptions have a trend of migration towards the west. (2) The distance of migration of the center of gravity for residential electricity consumption is the highest, and the trend of migration of the center of gravity for industrial and total final electricity consumptions are synchronous. (3) Economic development is the main reason for the growth in regional electricity consumption, and the decrease in the investment electricity consumption intensity inhibits the growth of electricity consumption. This study provides references to restrain the excessive increase in electricity consumption and improve the layout of power facilities at the regional level.

A study of energy consumption for office buildings in Vietnam for sustainable energy and climate change mitigation

This study used survey data to evaluate the current status of electrical energy use of commercial office (CO) and governmental office (GO) buildings in Hanoi and Ho Chi Minh City (HCMC) in Vietnam. Main data used in the analyses including floor area and monthly electricity consumption which were gathered from the questionnaire survey for 57 CO and GO buildings in two cities – conducted by the Vietnam Clean Energy Program in 2015. The basic statistical analysis techniques were used to evaluate the electricity consumption intensity of office buildings and preliminarily analyze key factors affecting the energy consumption of these buildings. The results generally showed that CO buildings consumed significantly more electricity than GO buildings in all months of the year and the variation and trend of mean monthly electricity consumption intensity of the buildings strongly depend on that of ambient air temperature. In addition, the energy use intensities (EUIs) of CO buildings, GO buildings, and both buildings (CO+GO) in HCMC (in a hot climate all year round) were higher than those in Hanoi (in a climate with four distinct seasons). The mean EUIs of these buildings in Hanoi, HCMC, and both cities were 105.9, 116.4, and 109.6 kWh/m2 floor. year. The findings of this study are useful information on the status of energy use and energy efficiency of CO and GO buildings in Hanoi and HCMC, thereby contribute scientific bases to the development of policies and solutions to promote the energy efficiency of CO and GO buildings in the coming years and urgent actions for sustainable energy and climate change mitigation in Vietnam.

The threshold effect of electricity consumption and urbanization on carbon dioxide emissions in Ghana

PurposeThe quest for economic development has brought adverse effects on the environment through the release of greenhouse gases, such as carbon dioxide (CO2). This will counter the efforts to achieve the Sustainable Development Goals (SDGs) by 2030. This study, therefore, investigates the effect of electricity consumption and urbanization on CO2 emissions in Ghana. Electricity consumption and urbanization are among the factors that can be used to reduce CO2 emissions.Design/methodology/approachFollowing the STIRPAT framework with the Hansen (2000) least squares threshold estimation strategy, the study employed annual time series data from 1971 to 2019.FindingsThe study revealed a single threshold effect of both electricity consumption and urbanization on CO2 emissions. Electricity consumption intensity reduces CO2 emission when electricity consumption is below the threshold (6287GWh) but increases when consumption passes the threshold. However, urbanization exerts a positive influence on CO2 emissions regardless the level of urbanization (either before or after the threshold point). Again, the empirical results revealed that the urbanization threshold moderates the effect of electricity consumption on CO2 emissions.Research limitations/implicationsPolicymakers have to consider redesigning the current urbanization mode to include some new-type urbanization elements.Originality/valueThe threshold effect of electricity consumption and urbanization on CO2 emissions in Ghana is examined using the Hansen (2000) least square method.

Decomposition the driving force of regional electricity consumption in Japan from 2001 to 2015

Investigating electricity consumption is vital for identifying the driving forces for national onsite energy use and pathways for mitigating greenhouse gas emissions. However, before any electricity conservation measures are implemented, a well understanding of the driving forces behind sectoral electricity consumption variation are of great importance. Therefore, this research constructed an assessment framework on how electricity consumption reflects and responds to social and natural events, and how far the Japanese electricity consumption decouples economic development by estimating the changes in electricity consumption in Japan from 2001 to 2015. The results revealed that electricity consumption intensity and economic activity effect were both dominant but obtained opposite impacts in terms of electricity consumption changes. Moreover, differences in driving forces that caused spatial variance for regional changes in electricity consumption were found during the economic recession and recovery periods. The decoupling status showed that the decoupling degree in Japan changed from an extensive negative decoupling state to a weak decoupling state during the study periods. Subsequently, the results also shed light on how to mitigate regional electricity consumption stress sustainably.

Energy intensity convergence in Iranian provinces: evidence from energy carriers' consumption intensity.

Investigating the energy intensity convergence in Iran, as a country with very high energy intensity in the world, is important in order to assess whether the government's energy policies were effective to reduce energy intensity and its externality effects. This research investigated the convergence of total energy intensity and energy carriers' consumption intensity in Iran and whether the energy intensity in the provinces with high energy intensity has been converged to the provinces with low energy intensity. To that end, the Markov chain method was used to investigate the convergence of total energy intensity and energy carriers' consumption intensity in Iranian provinces from 2002 to 2016. The results indicated that there was club convergence in total energy, natural gas, electricity, and oil consumption intensity. Therefore, provinces with similar levels of energy intensity (e.g., high energy intensity and low energy intensity) converged to a unique steady state.

Using Spatial LMDI to Decompose Electricity Consumption Intensity in Shandong Province from 2015

In this paper, through the construction of spatial LMDI decomposition framework, we decompose the influencing factors of Shandong Province's total electricity consumption intensity from 2015 to 2018 into two parts: city’s economic growth factor and city’s electricity consumption intensity factor. The results show that, in this period, the increase of city’s electricity consumption intensity is the main reason for the increase of total electricity consumption intensity in Shandong Province. However, the city’s economic growth slightly slow down the increase of the total electricity consumption intensity. Compared with other cities, the growth of electricity consumption intensity in Binzhou is the main driving force of total intensity growth, accounting for 69.35%. However, 6 cities (Dezhou, Jining, Jinan, Laiwu, Liaocheng, Zibo) play a negative role in the rise of Shandong's overall electricity consumption intensity.

Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity.

The COVID-19 pandemic has disrupted economic activity in India. Adjusting policies to contain transmission while mitigating the economic impact requires an assessment of the economic situation in near real-time and at high spatial granularity. This paper shows that daily electricity consumption and monthly nighttime light intensity can proxy for economic activity in India. Energy consumption is compared with the predictions of a consumption model that explains 90 percent of the variation in normal times. Energy consumption declined strongly after a national lockdown was implemented on March 25, 2020 and remained a quarter below normal levels throughout April. It recovered subsequently, but electricity consumption remained lower even in September. Not all states and union territories have been affected equally. While electricity consumption halved in some, it declined very little in others. Part of the heterogeneity is explained by the prevalence of COVID-19 infections, the share of manufacturing, and return migration. During the national lockdown, higher COVID-19 infection rates at the district level were associated with larger declines in nighttime light intensity. Without effectively reducing the risk of a COVID-19 infection, voluntary reductions of mobility will hence prevent a return to full economic potential even when restrictions are relaxed. Together, daily electricity consumption and nighttime light intensity allow monitoring economic activity in near real-time and high spatial granularity.

Social Norms and Energy Conservation Beyond the US

The seminal studies by Allcott and Mullainathan (2010), Allcott (2011), and Allcott and Rogers (2014) show that social comparison-based home energy reports (HER) are a cost-effective climate policy intervention in the US. Our paper demonstrates the context-dependency of this result. In most industrialized countries, average electricity consumption and carbon intensity are well below US levels. Consequently, HER interventions can only become cost-effective if treatment effect sizes are substantially higher. For Germany, we provide evidence from a large-scale randomized controlled trial that effect sizes are in fact considerably lower than in the US. We conclude by illustrating that targeting highly responsive subgroups is crucial to reach cost-effectiveness and by identifying the few countries in which HER are promising policy instruments.

Decomposition Analysis of Influencing Factors of Electricity Consumption Demand and Intensity under Shanghai’s Transition Development

With the transition development of economic structure and energy consumption structure, electricity consumption of the production sector in China has been growing rapidly since the 21st century. The degree of electrification in Shanghai has deepened and electricity plays an increasingly important role in the agriculture, manufacturing and service sectors. This paper analyses the influential factors of Shanghai electricity consumption demand and intensity in the production sector during 1998 and 2017 through Index Decomposition Method. The results show that the energy structure effect has a growing contribution to the electricity consumption demand growth for the production sector in Shanghai since 2008, which implies that the electrification trend in Shanghai has deepened. The impact of energy efficiency improvement in Shanghai overtakes the impact of economic scale effect and energy structure effect, thus leading to electricity intensity decrease in the production sector. Besides, energy efficiency improvement in the manufacturing sector contributes most to power saving in Shanghai. Economic structure transition also contributes to electricity intensity decrease in Shanghai due to the decrease in the scale of the manufacturing sector. So, it is necessary to further tap the space for energy efficiency improvement in manufacturing and service industries and promote electrification when making strategic decisions of transition development, thus leading to green transformation and development of economy and energy.