Carbon Emission Factors Research Articles

Energetic, economic and environmental analyses of frost-free air-source heat pump in multi-type buildings and different locations

Frost-free air-source heat pumps (FFASHPs) have gained increasing attention as an alternative to conventional air-source heat pumps (ASHPs), chillers and boilers (CBs), in the context of low-carbon space heating and cooling. Conducting energetic, economic, and environmental (3E) analyses on these systems in various building types and locations is crucial for system selection, yet relevant research is scarce. To address this issue, this paper presents a comprehensive framework for 3E analyses, including location selection, building assessment, load calculation, system sizing and modeling, annual simulations, and subsequent data visualization. The study includes seven building types and 353 locations with specific climate conditions. The results indicate that FFASHPs outperform ASHPs in terms of energy efficiency, lifecycle costs, and carbon emissions. Compared to CBs, FFASHPs exhibit superior energy efficiency across most regions and building types, except for severe cold, hot summer and warm winter regions. In central and coastal areas, the advantages are more prominent due to higher natural gas prices, making FFASHPs commercially viable. In hot summer and cold winter region with electricity carbon emission factors ranging from 0.3574 to 0.6829 kgCO2/kW, the FFASHP exhibits significant potential for decarbonization due to its high efficiency. In renewable energy-rich areas with low emission factors (0.1031–0.2602 kgCO2/kWh), FFASHPs can achieve significant carbon reductions, up to 90 %. This paper, for the first time, achieves a comprehensive 3E analyses of FFASHPs, ASHPs, and CBs. It offers a general methodology, extensive datasets, and visualized graphs for system selection, promoting low-carbon space heating and cooling.

Analysis of the decoupling effect and driving factors of carbon emissions from the transportation sector in Guangdong Province

To propel the green transformation of the transportation industry in Guangdong Province. Against the backdrop of the five-year plan, this study employs the Tapio decoupling model and the Logarithmic Mean Divisia Index decomposition method to analyze the decoupling status and driving factors of carbon emissions from transportation and economic growth in Guangdong Province from 2001 to 2020. The results show that:(1) Both the total volume of carbon emissions from transportation and per capita emissions exhibit an overall upward trend, with petroleum-based emissions accounting for an overwhelming 96%, with diesel emissions register the highest proportion and most substantial increase. (2) The decoupling status predominantly manifests as weak decoupling, with intermittent years expansive coupling,, expansive negative decoupling, strong decoupling, and recessive coupling, thus indicating a persistent state of decoupling instability. (3) The study introduces the urbanization effect, wherein the effects of income urbanization, as well as spatial urbanization, predominantly exert positive driving influences. Conversely, the effects of energy intensity, population urbanization, industry scale, and energy structure collectively exert negative driving influences. Notably, the effect of income urbanization emerges as the primary factor propelling the augmentation of carbon emissions, while the energy intensity effect serves as the primary factor curbing such escalation. Finally, pertinent policy recommendations are put forth.

Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors

Analysis of the spatiotemporal distribution pattern and driving factors of carbon emissions has been a focus of research in recent years. However, at the county level, analyses of the driving factors of carbon emissions are still relatively few. This study selected the Guangxi Zhuang Autonomous Region as the research subject, selecting the normalized difference vegetation index (NDVI), nighttime light index (NLI), gross domestic product (GDP), and population density (POP) as driving factors. Based on the geographic detector method, the spatiotemporal distribution pattern and driving factors of carbon emissions at the county level in Guangxi were investigated. The results show the following: (1) There are significant regional differences in the degree of change in carbon emissions. From 2005 to 2020, the total carbon emissions in Guangxi show an upward trend, presenting a “high in the south and low in the north” distribution characteristic, gradually forming a high-level region in the capital city of Nanning, the city of Liuzhou, and some coastal cities (such as the cities of Qinzhou, Beihai, and Fangchenggang) as the core of carbon emissions; (2) NDVI, NLI, GDP, and POP have a relatively high impact on the carbon emission pattern in Guangxi, and the impact of human activity intensity on carbon emissions is higher than that of the influencing factors of NDVI; (3) The interaction between NDVI, NLI, GDP, and POP has a significant impact on the carbon emission pattern. The aforementioned results can provide decision-making suggestions for the social and economic development of Guangxi, as well as the formulation of carbon sequestration policies.

Evaluation of Carbon Emission Factors in the Cement Industry: An Emerging Economy Context

The cement industry is a major contributor to carbon emissions, responsible for 5–8% of global emissions. This industry is expanding, particularly in emerging economies, and it is expected that CO2 emissions will rise by 4% by 2050. To address this critical concern, this paper identifies ten factors that contribute to carbon emissions in the cement production process through an extensive literature review and prioritises these factors using the Bayesian best–worst method. The data was gathered by conducting a methodical online survey with seven cement industry professionals in Bangladesh, with the aim of gaining insights into the emerging economy. The results illustrate that fuel burning and electricity consumption are the two greatest contributors to CO2 emissions in the cement production process. This research provides guidelines for cement industries in emerging economies on how to reduce CO2 emissions as well as suggesting areas of future research for sustainable cement production.

Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model

Since China joined the WTO, its economy has experienced rapidly growth, resulting in significantly increase in fossil fuel consumption and corresponding rise in CO2 emissions. Currently, China is the world's largest emitter of CO2, the regional distribution is also extremely uneven. so, it is important to identify the factors influence CO2 emissions in the three regions and predict future trends based on these factors. This paper proposes 14 carbon emission factors and uses the random forest feature ranking algorithm to rank the importance of these factors in three regions. The main factors affecting CO2 emissions in each region are identified. Additionally, an ARIMA + LSTM carbon emission predict model based on the inverse error combination method is developed to address the linear and nonlinear relationships of carbon emission data. The findings suggest that the ARIMA + LSTM is more accurate in predicting the trend of CO2 emissions in China. Moreover, the ARIMA + LSTM is employed to forecast the future CO2 emission trends in China's east, central, and west regions, which can serve as a foundation for China's CO2 emission reduction initiatives.

Study on the Decoupling Effect of Energy and Carbon Emissions in the Yangtze River Economic Belt under the Dual Carbon Target

To achieve the goal of "double carbon" is the only way for our country to achieve emission reduction strategy. The Yangtze River Economic Belt is one of the strategic support belts for emission reduction, so it is of great significance to study the decoupling effect of energy and carbon emissions. Based on the carbon emission data of the Yangtze River Economic Belt from 1995 to 2021, this paper firstly analyzes the basic characteristics and spatial pattern of carbon emission in the Yangtze River Economic Belt. Secondly, a spatial econometric model is constructed to decompose the main influencing factors of carbon emissions in the Yangtze River Economic Belt, and the main influencing factors of economic and energy decoupling in the Yangtze River Economic Belt are explored by using the canonical correlation analysis method. Finally, BP neural network is used to forecast and analyze the carbon emission of the Yangtze River Economic Belt from 2022 to 2030. The results show that: (1) The center of gravity of carbon emissions in the Yangtze River Economic Belt is gradually shifting to the southeast, and there is a spatial correlation between regional carbon emissions. (2) The strong influencing factors of carbon emission in the Yangtze River Economic Belt are industrial composition and development differences; The influencing factors are city size and resource consumption intensity. The weak factor is the level of energy #consumption. The level of energy consumption and industrial composition are the main influencing factors of economic and energy decoupling in the Yangtze River Economic Belt. (3) Carbon emissions in the Yangtze River Economic Belt will continue to grow from 2022 to 2030, and will grow fastest from 2029 to 2030.

Modelling a sustainable supply chain with variable production and ambiguous carbon emission factor using a genetic algorithm

Modelling a sustainable supply chain with variable production and ambiguous carbon emission factor using a genetic algorithm

CO2 emissions change in Tianjin: The driving factors and the role of CCS

The environmental damage caused by the greenhouse effect is intensified by the continuous rise of carbon dioxide emissions. As a major industrial base in northern China, Tianjin has been emitting more carbon and thus faces a greater challenge to reduce its emissions. To meet the dual carbon goals of Tianjin, it is critical to identify influencing factors of carbon emissions and to find suitable technologies to effectively reduce emissions. In this study, the energy consumption data of Tianjin from 2005 to 2020 were analyzed via the Logarithmic Mean Divisia Index (LMDI). The carbon emission driving factors were decomposed and the contributions of four primary factors, including energy structure, energy intensity, gross domestic product (GDP) per capita, and population to CO2 emissions of this region, were evaluated. The ideal characteristics of the oil reservoirs with low permeability and the suitable carbon dioxide sinks make carbon capture and storage(CCS) technology implementation feasible in Tianjin. Despite this, the effect of CCS technology on CO2 emissions in the future is still uncertain. Thus, the Bass model was firstly introduced to assess the future development trend of CCS technology and its capacity of CO2 emission reduction. The results revealed that energy intensity was the primary contribution to reducing carbon emissions, while GDP per capita was the main contributor to the overall increase of CO2 emissions in 2005–2020. The CCS technology diffusion process is fast, with rapid growth starting in 2030 and reaching growth saturation in 2040. According to the scenario analysis, CCS could make a substantial contribution of 2674.78× 104 tons per year after 2040 to the carbon emission reduction of Tianjin. Therefore, the implementation of energy conservation and emission reduction policies as well as the expansion of CCS projects are essential to ensure that Tianjin achieves its carbon neutrality goal by 2060. This study offers helpful guidance for other cities to forecast their carbon emission reduction trajectories and establish emission reduction strategies.

Assessing Carbon Emissions from Biomass Burning in Croplands in Burkina Faso, West Africa

Agricultural biomass burning plays a critical role in carbon emissions, with implications for climate change. This study aims to assess carbon (C) emissions and establish C, CO, CO2 and CH4 emission factors (EFs) by simultaneously testing the effects of climatic conditions and cropland category on gas emissions. In Burkina Faso, 96 experimental fires were conducted in accordance with farmers’ operations during the land-clearing season in two climatic zones (Sudanian and Sudano-Sahelian) and across two cropland categories (Cropland Remaining Cropland (CC) and Land Converted to Cropland (LC)). The carbon mass balance technique was applied to estimate emissions. Climate zone and cropland category significantly influenced carbon emissions and emission factors (p < 0.05). The Sudanian zone recorded the highest carbon emissions (0.24 ± 0.01 t C ha−1). For cropland category, LC recorded the highest carbon emissions with an average value of 0.27 ± 0.01 t C ha−1. CO2 EFs ranged from 1661.44 ± 3.63 g kg−1 in the Sudanian zone to 1716.51 ± 3.24 g kg−1 in the Sudano-Sahelian zone. EFs showed a dependence on the cropland category, with the highest EFs in CC. Smart agricultural practices limiting cropland expansion and biomass burning need to be promoted. This study provides vital information useful for supporting decision making as part of Nationally Determined Contributions.

Measurement of carbon effect in land consolidation projects and evaluation of low-carbon promotion paths: a case study of Wudi County, Shandong Province, China.

Against the backdrop of China's "double carbon" objective, the exploration of low-carbon land consolidation has become a prominent area of focus for enhancing the development of ecological civilization. In this study, three typical projects at different time points (2016, 2019, and 2022) in Wudi County were selected to measure the carbon effect of land consolidation from four perspectives: artificial and industrial materials, mechanical shift consumption, land use structure, and farmland ecosystem. Based on the calculation of carbon effect of land consolidation by using carbon emission coefficient method, the changes of land use structure and carbon storage of farmland ecosystem before and after land consolidation were corrected by using GIS tools and net ecosystem productivity (NEP) model based on remote sensing technology, and the carbon emission intensity of each land consolidation project was finally obtained. The study summarized the influencing factors of carbon emissions through the above analysis and uses the fuzzy interpretation structure (FISM) model to provide the hierarchy of influencing factors of carbon emissions, thus proposing a low-carbon promotion path for land consolidation. The findings of this study can serve as a useful reference for low-carbon land consolidation efforts. The results showed that (1) the first, second, and third projects emitted 6140.06 t, 1243.78 t, and 17,604.62 t of carbon, respectively. Among them, the largest contributors to these emissions were labor and industrial materials, followed by mechanical shift; land use structure and farmland ecosystem were the main sources of carbon sinks and have a positive impact on the carbon cycle. (2) The carbon emission intensity of project one, project two, and project three after standardization is 0.26, 0.49, and 0.25, respectively, which are all at a high level. (3) According to the FISM model categorized 15 low-carbon upgrading paths, it was recommended that the government take a leading role in Wudi County by developing a scientific and rational construction plan. Additionally, efforts were made to actively protect farmland and forest land from destruction, reduce energy and material consumption, increase carbon storage in the farmland ecosystem, and promote low carbonization of land consolidation to the fullest extent possible.

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.

Hunting sustainable refrigerants fulfilling technical, environmental, safety and economic requirements

An urgent action to mitigate climate change is the replacement of hydrofluorocarbons used in refrigeration systems, for low-global warming potential (GWP) alternatives, with stringent legislations in place. However, very limited options exist for single-component low-GWP refrigerants with similar technical performance. This work presents an integrated approach for the rational design of low-GWP refrigerant blends, focused on replacing R134a (GWP 1430), and R410A (GWP 2088), used in automotive and domestic air-conditioning, and in high pressure commercial and industrial refrigeration, respectively. A 4 E analysis (energy, exergy, economic and environmental) was employed to select suitable candidates. The molecular polar soft-SAFT theory was used in a predictive manner to fill the gap in thermodynamic data required for evaluating the technical performance based on initial selection criteria including GWP, toxicity, flammability, and degree of azeotropy. Potential candidates from a pool of 432 options were narrowed down and evaluated using a drop-in analysis to replace the working fluids R134a and R410A in today's refrigeration and air conditioning cycles, resulting in seven promising blends. Environmental and cost rate evaluation (geographically dependent based on electricity cost, HFC utilization taxes, and carbon emission factors) quantified the impact associated with their use and emissions, allowing to identify suitable replacements: three blends for replacing R134a: (90/10) wt.% R1243yf + R152a, (90/10) wt.% R1243yf + R134a, and (60/40) wt.% R1243zf + R1234ze(E), and one for replacing R410A: (90/10) wt.% R1123 + R32. This work showcases the importance of using robust thermodynamic models in the search of low-GWP refrigerants blends, considering the 4 E criteria.

Low-carbon demand response strategy of buildings considering load rebound

With the carbon emission ratio of buildings increasing, including buildings in the carbon market, an inevitable trend of social development is created. Buildings are characterized by their strong seasonality, high simultaneity rate, and large load, which is not only the main factor leading to the peak load of a power grid but also an important resource for load control. Under the guidance of dynamic carbon emission prices and real-time electricity prices, it is worth considering how best to balance the peak response ability of buildings and the cause of the secondary peak leading to load rebound. Therefore, this paper proposes a low-carbon demand response strategy for buildings considering load rebound. First, a building load is classified based on how schedulable the load remains, and a building demand response model which considers how the load schedulability is constructed. Second, according to the different rebounds of different building loads, a load rebound optimization model based on linear attenuation was built, and the rebound parameters of various loads were designed. Then, the real-time carbon emission factor, load fluctuation reward, punishment factor, and real-time electricity price are introduced. Thus, the lowest running cost of the building load is taken as the objective function needed to construct a building demand response optimization model while also considering load rebound. Finally, an example analysis is used to verify the effectiveness of the proposed model in reducing the comprehensive operation cost of buildings. The proposed model not only effectively reduces the comprehensive operation cost of buildings, but also reduces carbon emissions and load fluctuations under the premise of satisfying the actual building load, which provides a new idea for the research of a low-carbon demand response strategy of buildings with load rebound.

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

Carbon emission calculation of prefabricated concrete composite slabs during the production and construction stages

Prefabricated buildings provide favorable technical support to achieve the "carbon peak and carbon neutral" target in the building field. The accurate and reliable quantification of building carbon emissions is an important basis to achieve the dual carbon target. However, research on the carbon emissions of prefabricated buildings is limited, and few studies have been conducted on the calculation of carbon emissions from prefabricated components. Based on the process inventory analysis method, the carbon emission calculation model for prefabricated components in the production and construction stages was established. The prefabricated concrete composite slabs and cast-in-place floor slabs of two typical residential buildings were selected for the case study, and a comparative study was conducted on their carbon emissions during the production and construction stages. The results indicated that the carbon emissions per unit area (net floor area) of prefabricated concrete composite slabs (43.31 kgCO2e/m2 and 43.01 kgCO2e/m2, respectively) were lower than those of cast-in-place floor slabs (46.32 kgCO2e/m2 and 46.09 kgCO2e/m2, respectively) in the production and construction stages. The carbon emissions in the production and construction stages of prefabricated concrete composite slabs accounted for about 97.45 % and 2.55 %, while those of cast-in-place slabs accounted for about 91.21 % and 8.79 %, respectively. The carbon emissions at the building material production stage mainly originated from rebar and cement. When the change rate of building materials consumption and materials carbon emission factors was the same, the change rate of carbon emissions per unit area was ranked as rebar > cement. Furthermore, the cases showed that the prefabrication rate of case B was higher than that of case A, but the carbon emissions per unit area of case B were lower than those of case A. This study provides a scientific calculation method for the carbon emissions of prefabricated components, as well as enriches the data for studying building carbon emissions.

Salinity is an important factor in carbon emissions from an inland lake in arid region

Saline lakes, serving as the ultimate destination for most hydrological systems, accumulate substantial amounts of nutrients and organic matter from basins, and act as vast carbon reservoirs. These lakes exhibit exceptionally active biogeochemical cycling processes of carbon dioxide (CO2) and methane (CH4), and constitute integral components of the global carbon cycle. However, understanding of greenhouse gas emissions from saline lakes remains unclear mostly due to scarce data. In this study, we obtained CO2 and CH4 diffusive fluxes and biogeochemical parameters during ice-free period of 2021 at Bosten Lake, which is a representative inland saline lake located in China's arid region. Results revealed that Bosten Lake was a significant source of atmospheric gas carbon emissions, with average diffusion emissions of 12.645 ± 3.475 mmol m−2 d−1 for CO2 and 0.279 ± 0.069 mmol m−2 d−1 for CH4. Temporally, field measurements found a positive correlation between conductivity (Spc, a proxy of salinity) and CO2 emissions (R2 = 0.50, p < 0.01). Furthermore, the CH4 diffusive fluxes increased with the trophic state index (TSI, R2 = 0.31, p < 0.01). Spatially, exogenous inputs led to the spatial heterogeneity of carbon emissions. Our results highlighted that temporal variations in salinity constitute a crucial factor influencing CO2 emissions, and the saline lake has greater global warming potential compared to freshwater. The study provides an in-depth analysis of greenhouse gas emissions and driving factors in saline lakes of arid regions, and supports a further understanding of the carbon cycle in different types of lakes.

Spatiotemporal evolution and driving factors of carbon emissions in Qingdao City based on GeoDetector

&lt;p&gt;Investigating the spatiotemporal characteristics and factors of urban carbon emissions is essential to reduce carbon emissions and achieve dual carbon goals. In this study, we examined the change tendency of carbon emissions using the coefficient of variation, the Sen’s slope method, and the Mann–Kendall (MK) test and explored the effects of socioeconomic and environmental variables on carbon emissions using GeoDetector, in Qingdao City. The results revealed that: (1) From 2000 to 2020, carbon emissions increased annually, the area ratio of high carbon emissions increased and of low carbon emissions decreased yearly. Over 73% of carbon emissions have changed in a moderate way (0.1&amp;lt; CV &amp;lt; 1) and 80% of Qingdao City experienced an increased tendency (β &amp;gt; 0) in carbon emissions. (2) Carbon emissions diminished gradually from the urban center to the periphery. There were significant spatiotemporal disparities from one another in the subareas, Municipal districts had the largest variation degree (CV=0.32) and a huge growth trend of carbon emissions, while Laixi, Jiaozhou, and Pingdu were minor. (3) Socio-economic factors demonstrated a stronger ability to explain carbon emissions than environmental factors. GDP density, population density and floor area ratio were the key variables that affect the spatial distribution of carbon emissions, and the interaction between GDPD and PD can explain 81.9% of the carbon emissions in Qingdao. New technologies and materials, low-carbon energy consumption and lifestyles, and acceptable economic growth were the main strategies for Qingdao to become a low-carbon city.&lt;/p&gt;&#x0D;

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

A probabilistic life-cycle assessment of carbon emission from magnesium phosphate cementitious material with uncertainty analysis

This study aims to quantify the carbon emission intensity of magnesium phosphate cementitious materials to explore its potential in carbon emission reduction. However, comprehensive research on the lifecycle carbon emissions of these materials remains scarce, plagued by issues of data uncertainty, accessibility, and scoping discrepancies. Therefore, a probabilistic lifecycle assessment approach was adopted in this study, enabling uncertainty analysis of the carbon emission intensity of magnesium phosphate cementitious materials. The findings indicate that the 10.5th percentile of the carbon emission intensity simulations for MPC stands at 0.624 kgCO2e/kg, suggesting that nearly 90% of the values surpass those of OPC, thereby refuting the presumption of its low-carbon attributes. Notably, the consumption and carbon emission factors of magnesium oxide and soluble phosphates emerge as pivotal influences on its carbon emissions. Additionally, a multiple linear regression model was employed to forecast the correlation between the constituents of magnesium phosphate cementitious materials and the volume of carbon emissions. Through this study, we can have a more comprehensive understanding of the environmental performance of magnesium phosphate cementitious materials in terms of carbon emission reduction, which will provide an important support for the selection and design of future green building materials.

Carbon Emission, Air Pollution, and Total Factor Productivity: Evidence from Prefecture-Level Cities in China

Based on the instrumental variable method and the panel data of 283 prefecture-level cities in China from 2004 to 2016, we explore the relationship between carbon emission, air pollution, and total factor productivity (TFP). The results show that: (1) Both carbon emission-induced and air pollution-induced environmental regulations positively promote TFP. For every 1% increase in the two indicators of the intensity of environmental regulation, carbon emission, and air pollution, TFP will increase by 1.7150 percentage points and 0.6366 percentage points respectively. (2) Carbon emission-induced and air pollution-induced environmental regulation have similar coefficients on TFP, indicating that the effects of the two are synergistic. Carbon emission has a greater impact on TFP, so enhancing environmental regulation on carbon emission can better promote the improvement of TFP. (3) Financial scale and government intervention have a significant intermediary effect between environmental regulation in both aspects and TFP.