A new method of hotspot analysis on the management of CO2 and air pollutants, a case study in Guangzhou city, China.

Emission inventory plays an important role in designing effective emission control strategies. Currently, there is unbalanced development of CO2 and air pollutant emission inventories in China and the spatial information of both cannot be obtained simultaneously, which prevents a collaborative control strategy. In this study, we developed a unified emission inventory including both CO2 and air pollutants, then utilized spatial mapping methods to identify the co-hotspots of both CO2 and air pollutants at a high spatial resolution (1 × 1 km2). We applied Guangzhou city as a case study to illustrate the method. The results showed that CO2 and air pollutants were mainly emitted from the stationary combustion sector and the transportation sector. These two sectors contributed 95 %, 67 %, and 93 % to total CO2, SO2, and NOx emissions, respectively. Up to 86 %, 86 %, 66 %, and 72 % of total CO2, SO2, NOx, and PM2.5 emissions were attributed to the top 10 % emission grids with 1 × 1 km2 resolution. However, our results showed high emission grids were not surrounded by other high emissions grids for all types of emissions analyzed in this study. The co-hotspot analysis enables accurate identification of high-emission grids, which helps environment managers to prioritize resource allocation when designing control strategies. Our study underscores the importance of managing CO2 and air pollutants simultaneously at the city level.

Allocating China's 2025 CO2 emission burden shares to 340 prefecture cities: methods and findings.

Peak emission is an important policy/scheme for all the countries to respond greenhouse gas mitigation. The key is how to distribute the emission burden shares to its sub-regions. This study aims to develop a prefecture city leveled CO2 emission allocation model by integrating multi-indicators method and benchmark method so that China's 2025 (end year of 14th Five-Year Plan, FYP) CO2 emission burdens can be allocated to its prefecture cities and provinces. Results show that China's total CO2 emission will reach 12 billion tons in 2025. The majority of such emission will occur in the east China due to its more developed economy and dense population. Cities with high emissions are usually allocated more emission quotas, such as Shanghai, Tianjin, Chongqing, Tangshan, Yulin, Suzhou, and Ningbo. The top five provinces with higher CO2 emission quotas are traditionally high-emission and energy-intensive provinces, including Shandong, Jiangsu, Inner Mongolia, Henan, and Hebei. The national CO2 emission intensity will decrease by 69.35% in 2025 compared to the 2005 level. The CO2 emission intensity reduction rates among the 340 Chinese cities is found to be fluctuating significantly from 16 to 74% during the 14th FYP. Finally, policy recommendations are raised for mitigating city level CO2 emissions by considering the local realities.

A systematic assessment of city-level climate change mitigation and air quality improvement in China.

China is facing dual challenges of air pollution and climate change. By using city-level data, we comprehensively assessed air quality and CO2 emission changes from 2015 to 2019 for 335 Chinese cities. We selected important regions for air pollution control and categorized all cities into different classes according to their development levels. Our novel approach revealed new insights on different patterns of changes of PM2.5, O3, and CO2 by region and city class. We found that PM2.5 concentrations decreased remarkably due to mandatory city-level reduction targets, especially in the Beijing-Tianjin-Hebei (-27%) region. Nonetheless, O3 concentrations and CO2 emissions increased in 91% and 69% of Chinese cities, respectively. Observed CO2 emission reductions in more developed cities were mainly due to prominent energy intensity reduction and energy structure improvement. Our study indicates a lack of synergy in air pollution control and CO2 mitigation under current policies in China. To address both challenges holistically, we suggest setting mandatory city-level CO2 emission reduction targets and reinforcing clean energy and energy efficiency measures.

Co-benefits of peaking carbon dioxide emissions on air quality and health, a case of Guangzhou, China.

Cities play a key role in making carbon emission reduction targets achievable and tackling air pollution. Using Guangzhou city as a case, this paper explored the air quality and health co-benefits of peaking carbon dioxide emissions under three scenarios and developed an integrated assessment framework by combining a local air pollutant emission inventory, an atmospheric chemistry transport model, and a health assessment model. The results showed that SO2, PM10, and PM2.5 could achieve larger emission reductions than NH3, VOCs, and NOx among all the scenarios we examined. Under the enhanced peaking scenario with the most stringent mitigation strategies, Guangzhou could meet the local ambient air quality standard for PM2.5 (34 µg/m3), with the most reduction observed in the annual average PM2.5 concentration (28.4%) and related premature deaths (17.08%), compared with the base year 2015. We also identified hotspot grids, which were areas with high concentrations of carbon emissions, high concentrations of air pollution and poor air quality in Guangzhou. Our analysis highlighted the importance of promoting peaking carbon dioxide emission for the improvement of air quality and public health at the city level.

Who is a good neighbor? Analysis of frontrunner cities with comparative advantages in low-carbon development.

A well-developed economy and low-carbon emission intensity are important characteristics of low-carbon cities; they also represent important tasks for achieving global climate change mitigation goals. It is seldom discussed, however, how we should identify frontrunner cities from which low-carbon development experiences can be gleaned and then implemented in neighboring cities. This study, therefore, proposed a simple indicator-the "good neighbor index"-to identify frontrunner cities in low-carbon transformation based on economic and emission performance. Based on this indicator, we identified "good neighbors" in static and dynamic views for China. The results showed that the static good neighbors in 2015 were mostly large cities with higher incomes and better industrial structures whereas the dynamic neighbors achieved better economic growth and emission reductions from 2005 to 2015, though their economic and emissions statuses were generally worse. The good neighbor list is not consistent with the list of national low-carbon pilot cities, which has largely overlooked the experiences of some fast-growing cities. These results have policy implications for the Chinese government in terms of promoting the low-carbon transformation of cities. The study can also provide a reference for other countries in addressing climate change at the city level.

CO2 emissions patterns of 26 cities in the Yangtze River Delta in 2015: Evidence and implications.

As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.

Community-wide assessment of protein-interface modeling suggests improvements to design methodology.

The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

Protein-protein docking with binding site patch prediction and network-based terms enhanced combinatorial scoring.

Protein-protein docking has made much progress in recent years, but challenges still exist. Here we present the application of our docking approach HoDock in CAPRI. In this approach, a binding site prediction is implemented to reduce docking sampling space and filter out unreasonable docked structures, and a network-based enhanced combinatorial scoring function HPNCscore is used to evaluate the decoys. The experimental information was combined with the predicted binding site to pick out the most likely key binding site residues. We applied the HoDock method in the recent rounds of the CAPRI experiments, and got good results as predictors on targets 39, 40, and 41. We also got good results as scorers on targets 35, 37, 40, and 41. This indicates that our docking approach can contribute to the progress of protein-protein docking methods and to the understanding of the mechanism of protein-protein interactions.