Predictions and driving factors of production-based CO2 emissions in Beijing, China

Liu, Zhao; Wang, Fang; Tang, Zhiyao; Tang, Jintong

doi:10.1016/j.scs.2019.101909

Cited by 55 publications

(15 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, urban emissions have been estimated at the wholecity scale using both top-down and bottom-up techniques as individual city studies or as collections of urban areas (Ramaswami and Chavez, 2013;Chen et al, 2019a;Harris et al, 2020;Jones et al, 2021;Meng et al, 2014;Shan et al, 2018;Shan et al, 2017;Zheng et al, 2021a;Long et al, 2021) as well as results focused on city results in England (Baiocchi et al, 2015), China (Liu et al, 2020a;Wang et al, 2017) and Europe (Baur et al, 2015). Many of these studies extend analysis to include Scope 3 or consumption emissions.…”

Section: Introductionmentioning

confidence: 99%

Estimating CO₂ emissions for 108 000 European cities

Moran

Pichler

Zheng

et al. 2022

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. City-level CO2 emissions inventories are foundational for supporting the EU's decarbonization goals. Inventories are essential for priority setting and for estimating impacts from the decarbonization transition. Here we present a new CO2 emissions inventory for all 116 572 municipal and local-government units in Europe, containing 108 000 cities at the smallest scale used. The inventory spatially disaggregates the national reported emissions, using nine spatialization methods to distribute the 167 line items detailed in the National Inventory Reports (NIRs) using the UNFCCC (United Nations Framework Convention on Climate Change) Common Reporting Framework (CRF). The novel contribution of this model is that results are provided per administrative jurisdiction at multiple administrative levels, following the region boundaries defined OpenStreetMap, using a new spatialization approach. All data from this study are available on Zenodo https://doi.org/10.5281/zenodo.5482480 (Moran, 2021) and via an interactive map at https://openghgmap.net (last access: 7 February 2022).

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating CO₂ emissions for 108 000 European cities

Moran

Pichler

Zheng

et al. 2022

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…A 1% increase in the proportion of the secondary industry’s added value leads to a 0.51% increase in CO 2 emissions. A 1% increase in the proportion of employed people in the secondary industry leads to a 0.21% increase in CO 2 emissions [ 59 ]. Yu et al used the STIRPAT model, which has been widely used in research on the impact of social and economic driving forces on the ecological environment [ 60 , 61 ], to analyse the influencing factors of CO 2 emissions in 20 low-carbon industrial parks in China and found that the increase in the proportion of tertiary industry had a significant impact on the reduction of CO 2 emissions [ 62 ].…”

Section: Industrial Transfer and Co 2 Emissions: Literature Review And Analysis Frameworkmentioning

confidence: 99%

Does Industrial Transfer Change the Spatial Structure of CO2 Emissions?—Evidence from Beijing-Tianjin-Hebei Region in China

Yue

Zhu

Yao

2021

IJERPH

View full text Add to dashboard Cite

As an important cause of global warming, CO2 emissions have become a research hotspot in recent years. Industrial transfer impacts regional CO2 emissions and is related to the low-carbon development of regional industries. Taking the Beijing-Tianjin-Hebei region (BTH region) as an example, this study analysed industrial transfer’s direct and indirect impacts on CO2 emissions based on a mediating model and two-way fixed effect panel regression. The results obtained indicate that industrial transfer-in has promoted CO2 emissions to a small extent, and the positive impact of industrial transfer-in on CO2 emissions wanes over time. Industrial transfer affects CO2 emissions by acting on the economic level, on population size, and on urbanisation level, but the indirect effect is weaker than the direct effect. Industrial transfer does not lead to technological upgrading, but the latter is an effective means of carbon emission reduction. Industrial transfer-in has shown a positive effect on CO2 emissions for most cities, but there are exceptions, such as Cangzhou. In the future, the BTH region should maintain coordinated development among cities and improve the cooperative innovation mechanism for energy conservation and emission reduction.

show abstract

“…In 2017, it was found that 81% of the GHG was CO 2 , which is a major contributor to climate change and global warming (Z. Liu et al, 2020).…”

Section: Overview and Motivationmentioning

confidence: 99%

“…GHG is the major factor that is negatively contributing to climate change (Z. Liu et al, 2020). Air pollutant NOx, which reflects on the public health aspect, represents a family of seven compounds (Cox, 1999;Price et al, 1997).…”

Section: E2ecav Environmental Analysismentioning

confidence: 99%

See 1 more Smart Citation

Advancements in multi-objective eco-routing solutions using connectivity

Alfaseeh¹

2021

Preprint

View full text Add to dashboard Cite

Due to the significant adverse impact of transportation systems on the environment, topics related to alleviating greenhouse gas (GHG) emissions are gaining more attention. As potential solutions to mitigate GHG emissions, several approaches have been proposed to better control traffic and manage transportation systems. The employment of Intelligent Transportation System (ITS), which adopts the advancements in Information and Communication Technology (ICT), has been proposed as the most favourable approach to alleviate the undesirable impact of transportation systems on the environment. ITS can control several aspects of a network, such as speed, traffic signals, and route guidance. For the purpose of routing, this research aims to exploit the advancements in ICT by including connected and automated vehicles (CAVs) and sensing technology in an urban congested network.<div>Anticipatory multi-objective eco-routing in a distributed routing framework was proposed and compared to myopic routing with a large case study on a congested network. The End-to-End Connected Autonomous Vehicles (E2ECAV) dynamic distributed routing framework was examined, and encouraging results were found based on the traffic and environmental perspectives. The impact of different market penetration rates (MPRs) of CAVs was examined for various traffic conditions. E2ECAV was adopted for both the myopic and anticipatory routing strategies in this dissertation. The best GHG costing approach was defined and was among the elements tackled in this research. For a robust anticipatory routing application, predictive models were developed based on Long-Short Term Memory (LSTM), a deep learning approach, while considering a high level of spatial (link level) and temporal (one minute) resolution. With regards to the LSTM predictive models, the impact was illustrated of using a deeper LSTM network and systematically tuning its hyper-parameters. The anticipatory routing strategy significantly outperformed the myopic routing strategy based on the the traffic and environmental perspectives. This research shows that ITS can help significantly reduce GHG emissions produced by transportation systems. The developed predictive models can be used while real-time data are collected from sensors within an urban network. Furthermore, the proposed anticipatory routing framework can be applied in a real-time situation. </div>

show abstract

Predictions and driving factors of production-based CO2 emissions in Beijing, China

Cited by 55 publications

References 31 publications

Estimating CO₂ emissions for 108 000 European cities

Estimating CO₂ emissions for 108 000 European cities

Does Industrial Transfer Change the Spatial Structure of CO2 Emissions?—Evidence from Beijing-Tianjin-Hebei Region in China

Advancements in multi-objective eco-routing solutions using connectivity

Contact Info

Product

Resources

About

Predictions and driving factors of production-based CO2 emissions in Beijing, China

Cited by 55 publications

References 31 publications

Estimating CO2 emissions for 108 000 European cities

Estimating CO2 emissions for 108 000 European cities

Does Industrial Transfer Change the Spatial Structure of CO2 Emissions?—Evidence from Beijing-Tianjin-Hebei Region in China

Advancements in multi-objective eco-routing solutions using connectivity

Contact Info

Product

Resources

About

Estimating CO₂ emissions for 108 000 European cities

Estimating CO₂ emissions for 108 000 European cities