A fine spatial resolution modeling of urban carbon emissions: a case study of Shanghai, China.

Quantification of fossil fuel carbon dioxide emissions (CEs) at fine space and time resolution is a critical need in climate change research and carbon cycle. Quantifying changes in spatiotemporal patterns of urban CEs is important to understand carbon cycle and development carbon reduction strategies. The existing spatial data of CEs have low resolution and cannot distinguish the distribution characteristics of CEs of different emission sectors. This study quantified CEs from 15 types of energy sources, including residential, tertiary, and industrial sectors in Shanghai. Additionally, we mapped the CEs for the three sectors using point of interest data and web crawler technology, which is different from traditional methods. At a resolution of 30 m, the improved CEs data has a higher spatial resolution than existing studies. The spatial distribution of CEs based on this study has higher spatial resolution and more details than that based on traditional methods, and can distinguish the spatial distribution characteristics of different sectors. The results indicated that there was a consistent increase in CEs during 2000-2015, with a low rate of increase during 2009-2015. The intensity of CEs increased significantly in the outskirts of the city, mainly due to industrial transfer. Moreover, intensity of CEs reduced in city center. Technological progress has promoted the improvement of energy efficiency, and there has been a decoupling between the economic development and CEs in the city was observed during in 2000-2015.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH4 ) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4  yr-1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

Ecological risk assessment of ecosystem services in the Taihu Lake Basin of China from 1985 to 2020.

There are tremendous theoretical, methodological and policy challenges in evaluating the impact of land-use change on the degradation of ecosystem services (ES) at the regional scale. This study addresses these challenges by developing an interdisciplinary methodology based on the Procedure for Ecological Tiered Assessment of Risk (PETAR). This novel methodology integrates ecological models with a land-use change model. This study quantifies the multi-dimensional degradation risks of ES in the Taihu Lake Basin (TLB) of China from 1985 to 2020. Four key ES related to water purification, water quantity adjustment, carbon sequestration and grain production are selected. The study employs models of Denitrification-Decomposition (DNDC), Soil-Water-Atmosphere-Plant (SWAP), Biome-BGC and Agro-ecological Zoning (AEZ) for assimilations. Land-use changes by 2020 were projected using a geographically weighted multinomial logit-cellular automata (GWML-CA) model. The results show that rapid land-use change has posed a great degradation risk of ES in the region in 1985-2020. Slightly less than two-thirds of the basin experienced degradation of ES over the 1985-2010 period, and about 12% of the basin will continue to experience degradation until 2020. Hot spots with severe deterioration in 2010-2020 are projected to be centered around some small and less developed cities in the region. Regulating accelerated urban sprawl and population growth, reinforcing current environmental programs, and establishing monitoring systems for observing dynamics of regional ES are suggested as practical counter-measures.

Carbon consequences and agricultural implications of growing biofuel crops on marginal agricultural lands in China.

Using marginal agricultural lands to grow energy crops for biofuel feedstocks is a promising option to meet the biofuel needs in populous China without causing further food shortages or environmental problems. Here we quantify the effects of growing switchgrass and Miscanthus on Chinese marginal agricultural lands on biomass production and carbon emissions with a global-scale biogeochemical model. We find that the national net primary production (NPP) of these two biofuel crops are 622 and 1546 g C m(-2) yr(-1), respectively, whereas the NPP of food crops is about 600 g C m(-2) yr(-1) in China. The net carbon sink over the 47 Mha of marginal agricultural lands across China is 2.1 Tg C yr(-1) for switchgrass and 5.0 Tg C yr(-1) for Miscanthus. Soil organic carbon is estimated to be 10 kg C m(-2) in both biofuel ecosystems, which is equal to the soil carbon levels of grasslands in China. In order to reach the goal of 12.5 billion liters of bioethanol in 2020 using crop biomass as biofuel feedstocks, 7.9-8.0 Mha corn grain, 4.3-6.1 Mha switchgrass, or 1.4-2.0 Mha Miscanthus will be needed. Miscanthus has tremendous potential to meet future biofuel needs, and to benefit CO(2) mitigation in China.