Global and local carbon footprints of city of Hong Kong and Macao from 2000 to 2015

Hong Kong and Macao are featured with their urban metabolism as they heavily rely on the energy and resource supply from other regions. However, a comprehensive perspective is lacked to depict their CO2 emissions due to the independence of statistical data. Here we analyze the carbon footprints of Hong Kong and Macao. The direct energy-related emissions (Scope 1), the emissions of cross-boundary electricity (Scope 2), and the embodied emissions associated with trade (Scope 3) are examined. Scope 1 carbon footprints of the two areas were stabilized at 50 Mt, accounting for 0.6% of those from Mainland China in 2018. Their global footprints were approximately three times of their Scope 1 emissions, accompanied by a continuous growth between 2000 and 2015, and the contribution of their local footprints has doubled on average. Their Scope 3 emissions were mainly due to the enormous unfavorable balance of trade. Meanwhile, the increasing impact of imports' higher emission intensity on their Scope 3 emissions should not be ignored. We suggest that Hong Kong and Macao should adjust their mitigation policies that focus only on Scope 1 emissions as developed cities outsourcing production through supply chains

CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model

Instruction tuning has recently been recognized as an effective way of aligning Large Language Models (LLMs) to enhance their generalization ability across various tasks. However, when tuning publicly accessible, centralized LLMs with private instruction data, privacy concerns are inevitable. While direct transfer of parameterized modules between models is a plausible approach to address this, its implications and effectiveness need further exploration. This paper focuses on Offsite-Tuning (OFT), a representative technique that transfers transformer blocks between centralized LLMs and downstream emulators. Given the limited understanding of the underlying mechanism of OFT, we perform an empirical analysis on LLMs from the perspectives of representation and functional similarity. Interestingly, our findings reveal a unique modular structure within the layers of LLMs that appears to emerge as the model size expands. Simultaneously, we note subtle but potentially significant changes in representation and intermediate predictions across the layers. Inspired by these observations, we propose CRaSh, involving Clustering, Removing, and Sharing, a training-free strategy to derive improved emulators from LLMs. CRaSh significantly boosts performance of OFT with billions of parameters. Furthermore, we investigate the optimal solutions yielded by fine-tuning with and without full model through the lens of loss landscape. Our findings demonstrate a linear connectivity among these optima falling over the same basin, thereby highlighting the effectiveness of CRaSh and OFT. The source code is publicly available at https://github.com/TsinghuaC3I/CRaSh.Comment: Accepted to EMNLP 2023 (Main Conference

Natural gas supply from Russia derived from daily pipeline flow data and potential solutions for filling a shortage of Russian supply in the European Union (EU)

Russia is the largest natural gas supplier to the European Union (EU). The invasion of Ukraine was followed by a cutoff of gas supplies from Russia to many EU countries, and the EU is planning to ban or drastically reduce its dependence on Russia. We provide a dataset of daily gas consumption in five sectors (household and public building heating, power, industry, and other sectors) with supply source shares in the EU27 (27 EU member countries) and UK from 2016 to 2022. The datasets are available at Zenodo platform: https://doi.org/10.5281/zenodo.7549233 (Zhou et al., 2022). The dataset separates the contributions of Russian imports, liquefied natural gas (LNG) imports, and other supply sources to both direct supply and storage supply for gas consumption estimations. The dataset was developed with a gas network flow simulation model based on mass flow balance by combining data from multiple datasets including daily ENTSOG (European Network of Transmission System Operators for Gas) pipeline gas transport and storage, ENTSOE (European Network of Transmission System Operators for Electricity) daily power production from gas, and Eurostat monthly gas consumption statistics per sector. The annual consumption data were validated against the BP Statistical Review of World Energy and Eurostat datasets. We secondly analyzed the share of gas supplied by Russia in each country to quantify the “gap” that would result from a cessation of all Russian exports to Europe. Thirdly, we collected multiple data sources to assess how national gaps could be alleviated by (1) reducing the demand for heating in a plausible way using the lower envelope of gas empirical consumption – temperature functions, (2) increasing power generation from sources other than gas, (3) transferring gas savings from countries with surplus to those with deficits, and (4) increasing imports from other countries like Norway, the USA, Australia, and northern African countries from either pipelines or LNG imports, accounting for existing capacities. Our results indicate that it should be theoretically possible for the EU to collectively make up for a sudden shortfall of Russian gas by combining the four solutions together, provided a perfect collaboration between EU countries and the UK to redistribute gas from countries with surplus to those with deficits. Further analyses are required to investigate the implications with respect to the costs, including social, economic, and institutional dimensions; political barriers; and negative impacts on climate policies, with inevitable increases in CO2 emissions if the use of coal is ramped up in the power sector.</p

Estimates of daily ground-level NO2 concentrations in China based on big data and machine learning approaches

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants. However, current ground-level NO2 concentration data are lack of either high-resolution coverage or full coverage national wide, due to the poor quality of source data and the computing power of the models. To our knowledge, this study is the first to estimate the ground-level NO2 concentration in China with national coverage as well as relatively high spatiotemporal resolution (0.25 degree; daily intervals) over the newest past 6 years (2013-2018). We advanced a Random Forest model integrated K-means (RF-K) for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, we also, for the first time, introduce socio-economic parameters to assess the impact by human activities. The results show that: (1) the RF-K model we developed shows better prediction performance than other models, with cross-validation R2 = 0.64 (MAPE = 34.78%). (2) The annual average concentration of NO2 in China showed a weak increasing trend . While in the economic zones such as Beijing-Tianjin-Hebei region, Yangtze River Delta, and Pearl River Delta, the NO2 concentration there even decreased or remained unchanged, especially in spring. Our dataset has verified that pollutant controlling targets have been achieved in these areas. With mapping daily nationwide ground-level NO2 concentrations, this study provides timely data with high quality for air quality management for China. We provide a universal model framework to quickly generate a timely national atmospheric pollutants concentration map with a high spatial-temporal resolution, based on improved machine learning methods

Tropospheric ozone (O3) pollution in Johannesburg, South Africa : exceedances, diurnal cycles, seasonality, Ox chemistry and O3 production rate

Ground-level ozone (O3) is an air pollutant of major health and environmental concern. The Johannesburg-Pretoria megacity in South Africa is the industrial and economical capital of the country with more than 10 million inhabitants experiencing poor air quality. In 2004, the City of Johannesburg (CoJ) began monitoring trace gases to assess ground-level O3 pollution. Here, we use CoJ’s publicly available air quality data, and present the first long-term data analysis of O3, nitric oxide (NO), nitrogen dioxide (NO2), NOx and carbon monoxide (CO) in the City from 2004 to 2011 at three air quality monitoring sites: Buccleuch, Delta Park and Newtown. We quantified CoJ’s South African National Ambient Air Quality Standards (NAAQS) exceedances for O3 and NO2, and demonstrate the City’s substantial O3 and NO2 air pollution problem. O3 mixing ratios peak in the early afternoon as expected due to photochemical production. To estimate O3 production rates, we summed O3 and NO2 diurnal profiles to obtain Ox mixing ratios at each site. This analysis provided insight into missing volatile organic compound (VOC) reactivity as well as primary NO2 emissions information necessary for developing tropospheric O3 pollution mitigation strategies. Furthermore, CoJ experiences high O3 mixing ratios on weekends due to lower NOx traffic emissions titrating the O3, thereby providing evidence of a VOC-limited regime for O3 production. Seasonal peak O3 occurs in the austral spring, a maximum that we link to increases in water (H2O) concentrations which in turn increases radical chemistry leading to O3. In addition, wintertime VOC and aerosol emissions from biomass burning over the winter add important precursors for O3 formation once radical chemistry is initiated during the first rain events in early spring. In all, this study will help inform air quality modelling and policy work on air pollutants in the City of Johannesburg, South Africa.https://www.cleanairjournal.org.zaam2024Geography, Geoinformatics and MeteorologySDG-11:Sustainable cities and communitie

Tropospheric ozone (O3) pollution in Johannesburg, South Africa: Exceedances, diurnal cycles, seasonality, Ox chemistry and O3 production rates

Ground-level ozone (O3) is an air pollutant of major health and environmental concern. The Johannesburg-Pretoria megacity in South Africa is the industrial and economical capital of the country with more than 10 million inhabitants experiencing poor air quality. In 2004, the City of Johannesburg (CoJ) began monitoring trace gases to assess ground-level O3 pollution. Here, we use CoJ’s publicly available air quality data, and present the first long-term data analysis of O3, nitric oxide (NO), nitrogen dioxide (NO2), NOx and carbon monoxide (CO) in the City from 2004 to 2011 at three air quality monitoring sites: Buccleuch, Delta Park and Newtown. We quantified CoJ’s South African National Ambient Air Quality Standards (NAAQS) exceedances for O3 and NO2, and demonstrate the City’s substantial O3 and NO2 air pollution problem. O3 mixing ratios peak in the early afternoon as expected due to photochemical production. To estimate O3 production rates, we summed O3 and NO2 diurnal profiles to obtain Ox mixing ratios at each site. This analysis provided insight into missing volatile organic compound (VOC) reactivity as well as primary NO2 emissions information necessary for developing tropospheric O3 pollution mitigation strategies. Furthermore, CoJ experiences high O3 mixing ratios on weekends due to lower NOx traffic emissions titrating the O3, thereby providing evidence of a VOC-limited regime for O3 production. Seasonal peak O3 occurs in the austral spring, a maximum that we link to increases in water (H2O) concentrations which in turn increases radical chemistry leading to O3. In addition, wintertime VOC and aerosol emissions from biomass burning over the winter add important precursors for O3 formation once radical chemistry is initiated during the first rain events in early spring. In all, this study will help inform air quality modelling and policy work on air pollutants in the City of Johannesburg, South Africa.

COVID-19 causes record decline in global CO2 emissions

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures