Research

@article{Jiang2022,

title = {Different Material Footprint Trends between China and the World in 2007-2012 Explained by Construction- and Manufacturing-associated Investment},

author = {Meng Jiang and Paul Behrens and Yongheng Yang and Zhipeng Tang and Dingjiang Chen and Yadong Yu and Lin Liu and Pu Gong and Shengjun Zhu and Wenji Zhou and Bing Zhu and Arnold Tukker},

url = {https://doi.org/10.1016/j.oneear.2021.12.011},

doi = {10.1016/j.oneear.2021.12.011},

issn = {25903322},

year  = {2022},

date = {2022-01-01},

journal = {One Earth},

volume = {5},

number = {1},

pages = {109–119},

publisher = {The Author(s)},

abstract = {Economic prosperity is vital to human development but relies heavily on material extraction, which causes environmental degradation. To successfully decouple growth from degradation, it is essential to identify the primary drivers of countries' material footprints (MFs). However, such understanding remains limited due to the complexities of investment- versus consumption-driven growth. Here, we focus on the MF of China between 2007 and 2012, which was responsible for 24%–30% of global material use. We adopt input-output models that consider trade and classify investment/consumption to estimate, at the province level, the relationship between MFs and the Human Development Index (HDI). Results show that during 2007–2012, China's per capita MF mirrored its HDI and increased by nearly 20%. This is especially prominent in poorer provinces. We further find that it was investments, particularly capital investments associated with construction and manufacturing, that dominated China's MF rather than a consumption-driven demand. Given vast infrastructure gaps in emerging economies and post-pandemic investment plans, these findings emphasize the need for a better understanding of the drivers of growth.},

keywords = {capital investment, China, circular economy, human development, Material footprint, resource efficiency},

pubstate = {published},

tppubtype = {article}

}

@article{Zhong2021ab,

title = {Global greenhouse gas emissions from residential and commercial building materials and mitigation strategies to 2060},

author = {Xiaoyang Zhong and Mingming Hu and Sebastiaan Deetman and Bernhard Steubing and Hai Xiang Lin and Glenn Aguilar Hernandez and Carina Harpprecht and Chunbo Zhang and Arnold Tukker and Paul Behrens},

url = {https://doi.org/10.1016/j.rser.2022.112677 https://doi.org/10.1016/j.energy.2022.123339 https://doi.org/10.1016/j.oneear.2021.12.011 https://doi.org/10.1016/j.ecolecon.2022.107339 https://doi.org/10.1016/j.jclepro.2021.127098 https://doi.org/10.1016/j.sci},

doi = {10.1038/s41467-021-26212-z},

issn = {20411723},

year  = {2021},

date = {2021-07-01},

journal = {Nature Communications},

volume = {12},

number = {1},

pages = {1–10},

publisher = {Elsevier Ltd},

address = {Copenhangen},

edition = {1},

abstract = {Building stock growth around the world drives extensive material consumption and environmental impacts. Future impacts will be dependent on the level and rate of socioeconomic development, along with material use and supply strategies. Here we evaluate material-related greenhouse gas (GHG) emissions for residential and commercial buildings along with their reduction potentials in 26 global regions by 2060. For a middle-of-the-road baseline scenario, building material-related emissions see an increase of 3.5 to 4.6 Gt CO2eq yr-1 between 2020–2060. Low- and lower-middle-income regions see rapid emission increase from 750 Mt (22% globally) in 2020 and 2.4 Gt (51%) in 2060, while higher-income regions shrink in both absolute and relative terms. Implementing several material efficiency strategies together in a High Efficiency (HE) scenario could almost half the baseline emissions. Yet, even in this scenario, the building material sector would require double its current proportional share of emissions to meet a 1.5 °C-compatible target.},

keywords = {Acoustic measurements, adaptation measure, Animal husbandry, Beach sampling, Beach sediment, beach sediment citizen science, Benefits, biodiversity impacts, biodiversity loss, Black carbon, capital investment, Carbon emissions, Caribbean, China, china-us trade conflicts, circular economy, Citizen science, climate change, CO2 emissions, CO2emissions, Community, Complex terrain, Concentrated solar power, Consultation, Consumption structure, countryside species-area relationship, Cress (Lepidium sativum), Decomposition analysis, Decoupling analysis, Desulfurization technologies, Economic and social impacts, Electricity generation, Electricity sector, Electricity system, Emission projections, energy footprint, Energy models, Energy scenarios, Energy systems, Energy technology, Energy transition, energy use, energy water nexus, Energy-water nexus, Environmental, Environmental impact, environmental inequality, Environmental Justice, Environmentally extended multiregional input-outpu, EU countries, Europe, Exposure concentration, Extraction, Feed-in tariffs, Feedback and spillover effects, Flow curvature, food system, Historical drivers, Household carbon emissions, Household GHG footprints, human development, Hybrid input-output analysis, Hydraulic unbalanced forces, hydro-turbine governing system, Hydroelectric generating systems, Income inequality, Industrial CO2 emissions, industrial ecology, Input-output analysis, input–output analysis, international trade, Investment, Investment and opportunity costs, Land, Land degradation, land-use intensity, Learning curve, Lesser Antilles, Lidar observations, Lidars, Life cycle assessment, Low-carbon electricity technology, Material footprint, Mathematical model, Method standardization, Microplastics, Mineral resources, Mining, Modal interactions, MRIO model, Multi-regional input-output (MRIO) analysis, Multi-Regional Input-Output Analysis, Multi-regional input-output model, multiregional input-output analysis, Nano- and micron-sized plastics, Non-fossil electricity, Operational, Perceptions, Plastic pollution, Policy goals, Poverty, Power density, power generation, Power plants, Power transmission, Primary crops, Public transit, Rebound, Remote sensing, renewable energy policy, resource efficiency, Responsibility Sharing, Sectoral analysis, Sharing economy, SO2 emission reduction, Socio-economic impact, Sodar, Solar energy, Spatial decomposition analysis, Spatial impact, Spatially explicit, Standard operating procedure, Sublethal impacts, Subnational, Subsystem input-output model, Sustainable consumption, techno-economic evaluation, Terrestrial systems, thermal emissions, Urban-rural differences, Urbanization, Vascular plant, Vulnerable passengers, water basin, water consumption, Water footprint, Water scarcity, Wind, Wind energy},

pubstate = {published},

tppubtype = {article}

}

@article{Yuan2019,

title = {Driving forces of household carbon emissions in China: A spatial decomposition analysis},

author = {Rong Yuan and Joao F. D. Rodrigues and Paul Behrens},

doi = {10.1016/j.jclepro.2019.06.110},

issn = {09596526},

year  = {2019},

date = {2019-01-01},

journal = {Journal of Cleaner Production},

volume = {233},

pages = {932–945},

abstract = {China has strong policies to increase consumption and rebalance the economy away from capital investments. Since the total household carbon emissions (HCEs) grew 19% between 2007 and 2012, the study of driving forces from the spatial perspective provides insights into meeting these policy goals. To understand how to reduce HCEs across China, we analyze the driving factors of urban and rural household carbon emissions (HCEs) in 30 provinces in 2012. We distinguish two emission sources: direct (e.g. combustion of coal in the home), and indirect (emissions occurring along the supply chain of goods and services consumed by households), and analyze five factors for both urban/rural and direct/indirect emissions: population, income per capita, emission factors (emission improvements in technology), energy/consumption structure (changes in consumption), and energy/consumption intensity. Moreover, we aggregate the first two factors as the ‘scale' effect and the latter three as the ‘effort' effect, and explore their decoupling relationship. We find that the scale effect from income and population was the major driver of high urban HCEs in most of coastal provinces, up to 222% higher than the national average. The high HCEs in non-coastal industrial provinces (e.g. Inner Mongolia) were due to the limited improvements in the effort effect, specifically limited technology improvements, up to 101% than the national average. Almost all urban areas in coastal provinces achieved strong or weak decoupling, with the northeastern, central, western provinces witnessing negative decoupling. Urban areas in the main industrial provinces (Hebei, Heilongjiang and Inner Mongolia) showed the largest negative decoupling with values of −2.2, −3.0 and −2.7, respectively, due to a larger share of coal consumption and higher energy intensity within urban areas. Therefore, under the background of regional convergence, making larger efforts to reducing the HCEs in non-coastal provinces is critical for promoting a low-carbon transition in the household consumption.},

keywords = {China, Decoupling analysis, Household carbon emissions, MRIO model, Spatial decomposition analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Yuan2018c,

title = {The evolution of inter-sectoral linkages in China's energy-related CO2emissions from 1997 to 2012},

author = {Rong Yuan and Paul Behrens and Joao F. D. Rodrigues},

url = {https://doi.org/10.1016/j.eneco.2017.11.022},

doi = {10.1016/j.eneco.2017.11.022},

issn = {01409883},

year  = {2018},

date = {2018-01-01},

journal = {Energy Economics},

volume = {69},

pages = {404–417},

publisher = {Elsevier B.V.},

abstract = {Energy-related CO2emissions in China have been extensively investigated. However, the mechanisms of how energy-related emissions are driven by inter-sectoral linkages remains unexplored. In this paper, a subsystem input-output model was developed to investigate the temporal and sectoral changes of emissions in China from 1997 to 2012. We decomposed total emissions into internal, spillover, feedback, and direct components. Our results show that the equipment manufacturing, construction and services sectors are the main sources of emissions during the whole period, which have a larger spillover component, primarily through indirect upstream emissions in the heavy-manufacturing, transportation, and power sectors. The emissions from the power and transportation sectors are dominated by direct rather than the spillover emissions. The shares of the feedback and internal components in the heavy manufacturing sectors were significantly higher than those of other sectors. Our results suggest that further addressing carbon emissions along the supply chain of equipment manufacturing, construction and services sectors, and improving technologies in the heavy manufacturing and power sectors holds important future opportunities for curbing the rapid growth of carbon emissions in China.},

keywords = {China, CO2emissions, Feedback and spillover effects, Sectoral analysis, Subsystem input-output model},

pubstate = {published},

tppubtype = {article}

}