Carbon emission drivers and coupling coordination analysis of residential central heating in northern China

BACKGROUND: Mounting evidence has shown that short-term exposure to ambient temperature can increase mortality. However, the health effects associated with long-term temperature are less clear. Furthermore, more research has been focused on the mortality instead of prevalence of chronic diseases. METHODS: We constructed two cohorts using China Family Panel Study (CFPS) and assigned annual means of wet bulb temperature and PM2.5 to study participants based on their ID code of residential. Cox proportional hazards models were used to examine the association between long-term exposure to ambient temperature, residential heating and diabetes incidence, adjusting for potential confounders. RESULTS: We found a non-linear relationship between the long-term temperature and diabetes in multipollutant model, which is a U-shaped curve (p<0.001). The optimal temperature to lower diabetes risk was around 12°C. In addition, spring temperatures were negatively associated with diabetes risk, with warm temperature being protective of diabetes; while autumn temperatures were positively associated with diabetes incidences, with warm temperature being harmful. Apart from the long-term temperature, residential heating was also great threat to the diabetes incidence, with a HR = 1.18 (95% CI: 1.03, 1.36). Finally, we evaluated the extra diabetes cases in China if (1) central heating is installed in Southern China and (2) under different climate change scenarios. CONCLUSION: Using two representative cohorts, we found that either higher or lower annual temperature deviating 12°C is harmful to the diabetes incidence. Therefore, the climate change will contribute different influence in Southern China and Northern China. To our surprise, residential heating is also bad for the diabetes incidence, which alert us that if it is necessary about the installing of residential heating in those areas without residential heating.

We investigated the decadal changes in the different types of summer mean precipitation over China across the mid-1990s based on observational datasets. The spatial variations in the observed decadal changes were estimated by comparing the present day (PD) time period of 1994–2011 with an earlier period of 1964–1981. The summer total precipitation increased in southern China and decreased in northern China from the early period to the PD. The increases of precipitation in southern China were due to increases in the frequency of heavy and moderate rainfall, whereas the decreases over northern China were mainly due to decreases in the frequency of moderate and light rainfall. Based on a set of numerical experiments using an atmospheric general circulation model coupled with a multilevel mixed-layer ocean model, we found that the increase of precipitation frequency forced by greenhouse gases is the main reason of increasing precipitation over southern and northeastern China, while the decrease of frequency caused by anthropogenic aerosol (AA) induces the decreasing precipitation over northern China. The water vapor flux convergence and water vapor flux strengthen in southern China and northeastern China by anthropogenic greenhouse gases. This distribution is also conducive to precipitation in most of southern China and northeastern China. Under the control of weakened southwesterly winds and 850-hPa divergence, precipitation decreases over northern and southwestern China by AA.

Regulation of biophysical drivers on carbon and water fluxes over a warm-temperate plantation in northern China

Abstract. China has pledged reduction of carbon dioxide (CO2) emissions per unit of gross domestic product (GDP) by 60 %–65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, the lack of observational data and disagreement among the many available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of control measures. To demonstrate the value of atmospheric observations for constraining CO2 inventories we track the ability of CO2 concentrations predicted from three different CO2 inventories to match a unique multi-year continuous record of atmospheric CO2. Our analysis time window includes the key commitment period for the Paris Agreement (2005) and the Beijing Olympics (2008). One inventory is China-specific and two are spatial subsets of global inventories. The inventories differ in spatial resolution, basis in national or subnational statistics, and reliance on global or China-specific emission factors. We use a unique set of historical atmospheric observations from 2005 to 2009 to evaluate the three CO2 emissions inventories within China's heavily industrialized and populated northern region accounting for ∼33 %–41 % of national emissions. Each anthropogenic inventory is combined with estimates of biogenic CO2 within a high-resolution atmospheric transport framework to model the time series of CO2 observations. To convert the model–observation mismatch from mixing ratio to mass emission rates we distribute it over a region encompassing 90 % of the total surface influence in seasonal (annual) averaged back-trajectory footprints (L_0.90 region). The L_0.90 region roughly corresponds to northern China. Except for the peak growing season, where assessment of anthropogenic emissions is entangled with the strong vegetation signal, we find the China-specific inventory based on subnational data and domestic field studies agrees significantly better with observations than the global inventories at all timescales. Averaged over the study time period, the unscaled China-specific inventory reports substantially larger annual emissions for northern China (30 %) and China as a whole (20 %) than the two unscaled global inventories. Our results, exploiting a robust time series of continuous observations, lend support to the rates and geographic distribution in the China-specific inventory Though even long-term observations at a single site reveal differences among inventories, exploring inventory discrepancy over all of China requires a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and nationally. We find that carbon intensity in the northern China region has decreased by 47 % from 2005 to 2009, from approximately 4 kg of CO2 per USD (note that all references to USD in this paper refer to USD adjusted for purchasing power parity, PPP) in 2005 to about 2 kg of CO2 per USD in 2009 (Fig. 9c). However, the corresponding 18 % increase in absolute emissions over the same time period affirms a critical point that carbon intensity targets in emerging economies can be at odds with making real climate progress. Our results provide an important quantification of model–observation mismatch, supporting the increased use and development of China-specific inventories in tracking China's progress as a whole towards reducing emissions. We emphasize that this work presents a methodology for extending the analysis to other inventories and is intended to be a comparison of a subset of anthropogenic CO2 emissions rates from inventories that were readily available at the time this research began. For this study's analysis time period, there was not enough spatially distinct observational data to conduct an optimization of the inventories. The primary intent of the comparisons presented here is not to judge specific inventories, but to demonstrate that even a single site with a long record of high-time-resolution observations can identify major differences among inventories that manifest as biases in the model–data comparison. This study provides a baseline analysis for evaluating emissions from a small but important region within China, as well a guide for determining optimal locations for future ground-based measurement sites.

Progress in energy-efficiency standards for residential buildings in China

Toward sustainable heating: Assessment of the carbon mitigation potential from residential heating in northern rural China

Policy on energy consumption of district heating in northern China: Historical evidence, stages, and measures

Investigation of variations, causes and component distributions of PM2.5 mass in China using a coupled regional climate-chemistry model

Residential heating using solid fuels contributes significantly to air pollution and has subsequent health impacts in China. To mitigate emissions, a clean heating campaign (CHC-1) covering 28 municipalities has been implemented. Although only a single penetration rate was initially planned by CHC-1 for all municipalities, outcomes in the different municipalities varied considerably. Recently, a second phase (CHC-2) has been launched for the remaining 128 municipalities in northern China with once again a fixed penetration rate set. Here, we quantified factors that affected the penetration rates of CHC-1, developed an intervention scheme with differentiated targets for CHC-2, and compared the environmental and health benefits of the fixed- and differentiated-rate strategies. We found that the penetration rates of CHC-1 depended on per capita income, terrain slope, and population density and that such relationships could be quantified using a piecewise regression model. This model was applied to develop a differentiated-rate strategy for CHC-2. It clearly evidenced that a differentiated scheme would be more environmentally beneficial. Although the same number of rural households can achieve clean heating under both intervention scenarios, the proposed differentiated strategy can prevent 30 000 (23 000-34 000) premature deaths associated with residential heating annually compared to the 26 000 (21 000-31 000) premature deaths prevented under the fixed-rate scheme. Differences among gender and age groups and the effects of urbanization and aging are also discussed.

Coupling coordination degree spatial analysis and driving factor between socio-economic and eco-environment in northern China

From January to February (J–F) 2022, Southern China (SC) experienced abnormally heavy precipitation, with the regionally averaged total precipitation reaching 248 mm, making it the second-largest value since 1961. It was the second highest value since 1961 with the anomaly being about 2.3 standard deviations (2.3ơ, using as the threshold for the 2022 event) above the climatology in the period of 1961-2005. This extreme event resulted in significant damage to transportation, power supply, and crop production. Decreases in precipitation occur in both HadGEM3 and CMIP6 simulations over SC, accompanied by anomalous positive sea level pressure (SLP) and an anomalous anticyclonic circulation over SC. The two most important anthropogenic forcings are greenhouse gases and atmospheric aerosols and they can have different effects on SC J–F precipitation. Anthropogenic influence reduced the likelihood of extreme events like 2022 by about 50% (55%) in HadGEM3 (CMIP6). Furthermore, the greenhouse gas (GHG) forcing leads to a rightward shift of probability density functions (PDFs) to a wetter climate relative to natural forcing (NAT), while the anthropogenic aerosol (AA) forcing shifts to a drier world. The estimated PGHG and PAA indicate that a ~26 years event becomes a ~15 years event with PRGHG=1.75(1.60–2.00) in GHG and a ~83 years event in AA with PRAA=0.31 (0.24–0.37). Analyses of single forcing experiments using CMIP6 model ensembles demonstrate different roles of changes in GHG and AA in J–F precipitation over SC with GHG forcing inducing an increase and AA forcing inducing a decrease. However, the magnitude of AA-induced precipitation decrease is larger than that of GHG-induced increase, leading to the reduced likelihood of the J–F precipitation event similar to that of 2022 in SC by the combined effect of anthropogenic forcing.

Study on regional carbon emission is one of the hot topics under the background of global climate change and low-carbon economic development, and also help to establish different low-carbon strategies for different regions. On the basis of energy consumption and land use data of different regions in China from 1999 to 2008, this paper established carbon emission and carbon footprint models based on total energy consumption, and calculated the amount of carbon emissions and carbon footprint in different regions of China from 1999 to 2008. The author also analyzed carbon emission density and per unit area carbon footprint for each region. Finally, advices for decreasing carbon footprint were put forward. The main conclusions are as follows: (1) Carbon emissions from total energy consumption increased 129% from 1999 to 2008 in China, but its spatial distribution pattern among different regions just slightly changed, the sorting of carbon emission amount was: Eastern China > Northern China > Central and Southern China > Southwest China > Northwest China. (2) The sorting of carbon emission density was: Eastern China > Northeast China > Central and Southern China > Northern China > Southwest China > Northwest China from 1999 to 2003, but from 2004 Central and Southern China began to have higher carbon emission density than Northeast China, the order of other regions did not change. (3) Carbon footprint increased significantly since the rapid increasing of carbon emissions and less increasing area of productive land in different regions of China from 1999 to 2008. Northern China had the largest carbon footprint, and Northwest China, Eastern China, Northern China, Central and Southern China followed in turn, while Southwest China presented the lowest area of carbon footprint and the highest percentage of carbon absorption. (4) Mainly influenced by regional land area, Northern China presented the highest per unit area carbon footprint and followed by Eastern China, and Northeast China; Central and Southern China, and Northwest China had a similar medium per unit area carbon footprint; Southwest China always had the lowest per unit area carbon footprint. (5) China faced great ecological pressure brought by carbon emission. Some measures should be taken both from reducing carbon emission and increasing carbon absorption.

China accounts for half of the world’s annual coal consumption. Coal is the primary energy source for heating in urban areas, particularly in northern China. This causes significant challenges for urban air quality problems in China and greenhouse gases emissions. Urban district heating (DH) systems penetration is very high in northern China. It supplies space heating to more than 80% of urban buildings in the area. Unlike the electricity and transportation sectors, the heating sector has received little attention from policy makers and researchers in China, DH systems are an enabling infrastructure which facilitates energy efficiency improvements and the use of renewable energy sources. This study explores the dynamics and possibility to expand alternative energy sources (natural gas, biomass, direct geothermal heat, ground-source heat pump, municipal waste heat, industrial waste heat) for DH in China. We apply an analytical framework largely based on the multi-level perspective in socio-technical transitions theory, in which transitions are interpreted as the result of the functioning of niche, regime and landscape elements, and interactions between them. The study provides an integrated picture of the socio-technical structure and functioning of DH in China. The results show that an energy transition in Chinese DH systems has barely started. The system is characterised by stability of the coal-based DH regime, while a number of alternative niches are struggling to emerge. Among these, natural gas is the most successful example. However, at local level different niches present opportunities in terms of physical availability, economic viability and technical capacity to address changes in landscape pressures. A sustainable heat roadmap based on integrated energy planning and policy attention at the national level could be developed as one mechanism for instigating a much needed energy transition in DH in China.

Residential space heating remains a major source of greenhouse gas emissions in the building sector. In Germany, space heating accounts for the largest share of residential energy consumption, and accurate quantification of associated emissions is essential to meet national climate mitigation targets.Most research on residential heating emissions focuses on the regional or national levels, while estimates at finer spatial scales remain limited. Data availability further constrains the transferability and usability of current models. Consequently, approaches that deliver spatially and temporally detailed emission estimates and interactive tools to support analysis and decision-making by stakeholders are urgently needed.We introduce the Climate Action Navigator (CAN), a dashboard for the analysis and visualization of climate mitigation and adaptation spatial data, based entirely on open science principles. One of the tools available in the CAN estimates carbon dioxide emissions from residential heating at fine spatial at temporal scales. The tool applies a bottom-up accounting methodology at 100 m spatial resolution based on publicly available census and building characteristics data in Germany, including building age and dominant energy carriers. The resulting emission estimates are consistent with official city- and national-level inventories, confirming methodological reliability. Germany-wide analyses reveal strong spatial heterogeneity in energy consumption and emissions that correlate with urban morphological characteristics.Temporal dynamics are captured through an hourly simulation using the Demand Ninja model based on local weather data. The resulting temporal emission patterns can support inverse emission modelling applications as well as aid energy management by, for example, revealing peak heating demand times and locations.Results are delivered via the CAN interface as intuitive, interactive maps and charts that allow users to compare across neighborhoods, explore temporal emission dynamics, and assess potential mitigation actions. By integrating open-source data with high-resolution modeling and visualization, the Climate Action Navigator bridges the gap between scientific emission quantification and practical decision making. The approach supports transparent attribution and tracking of residential space-heating emissions, thereby advancing evidence-based climate mitigation planning.

The effects of increasing sea surface temperature (SST) and aerosol loading in a drought region in Southern China are studied using aerosol optical depth (AOD), low-level cloud cover (LCC), visibility, and precipitation from observed surface data; wind, temperature, specific humidity, and geopotential height from the NCEP–NCAR reanalysis fields; and SST from the NOAA archive data. The results show a warming of the SST in the South China Sea and the Indian Ocean, and a strengthening of the West Pacific Subtropical High (WPSH) in the early summer during the last 40 yr, with the high pressure system extending farther westward over the continent in Southern China. Because the early summer average temperature contrast between the land and ocean decreased, the southwesterly monsoon from the ocean onto mainland China weakened and a surface horizontal wind divergence anomaly occurred over Southern China stabilizing the boundary layer. Thus, less moisture was transported to Southern China, causing a drying trend. Despite this, surface observations show that AOD and LCC have increased, while visibility has decreased. Precipitation has decreased in this region in the early summer, consistent with both the second aerosol indirect effect (reduction in precipitation efficiency caused by the more numerous and smaller cloud droplets) and dynamically induced changes from convective to more stratiform clouds. The second aerosol indirect effect and increases in SST and greenhouse gases (GHG) were simulated separately with the ECHAM4 general circulation model (GCM). The GCM results suggest that both effects contribute to the changes in LCC and precipitation in the drought region in Southern China. The flooding trend in Eastern China, however, is more likely caused by strengthened convective precipitation associated with increases in SST and GHG.