How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method
Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impac...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wei, Wei [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Self-assembled 3D hierarchical MnCO - Rajendiran, Rajmohan ELSEVIER, 2020, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:321 ; year:2021 ; day:25 ; month:10 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jclepro.2021.128933 |
|---|
| Katalog-ID: |
ELV055384382 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV055384382 | ||
| 003 | DE-627 | ||
| 005 | 20230626041611.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220105s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jclepro.2021.128933 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica |
| 035 | |a (DE-627)ELV055384382 | ||
| 035 | |a (ELSEVIER)S0959-6526(21)03126-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 540 |q VZ |
| 084 | |a 35.18 |2 bkl | ||
| 100 | 1 | |a Wei, Wei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method |
| 264 | 1 | |c 2021transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. | ||
| 520 | |a Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. | ||
| 650 | 7 | |a Nighttime light data |2 Elsevier | |
| 650 | 7 | |a Impact factors |2 Elsevier | |
| 650 | 7 | |a Mainland China |2 Elsevier | |
| 650 | 7 | |a CO2 emissions |2 Elsevier | |
| 650 | 7 | |a Spatiotemporal variations |2 Elsevier | |
| 700 | 1 | |a Zhang, Xueyuan |4 oth | |
| 700 | 1 | |a Zhou, Liang |4 oth | |
| 700 | 1 | |a Xie, Binbin |4 oth | |
| 700 | 1 | |a Zhou, Junju |4 oth | |
| 700 | 1 | |a Li, Chuanhua |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Rajendiran, Rajmohan ELSEVIER |t Self-assembled 3D hierarchical MnCO |d 2020 |g Amsterdam [u.a.] |w (DE-627)ELV003750353 |
| 773 | 1 | 8 | |g volume:321 |g year:2021 |g day:25 |g month:10 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jclepro.2021.128933 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 35.18 |j Kolloidchemie |j Grenzflächenchemie |q VZ |
| 951 | |a AR | ||
| 952 | |d 321 |j 2021 |b 25 |c 1025 |h 0 | ||
| author_variant |
w w ww |
|---|---|
| matchkey_str |
weiweizhangxueyuanzhouliangxiebinbinzhou:2021----:odesaitmoavrainadmatatric2msindfearscteicianetgtoog |
| hierarchy_sort_str |
2021transfer abstract |
| bklnumber |
35.18 |
| publishDate |
2021 |
| allfields |
10.1016/j.jclepro.2021.128933 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica (DE-627)ELV055384382 (ELSEVIER)S0959-6526(21)03126-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Wei, Wei verfasserin aut How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations Elsevier Zhang, Xueyuan oth Zhou, Liang oth Xie, Binbin oth Zhou, Junju oth Li, Chuanhua oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:321 year:2021 day:25 month:10 pages:0 https://doi.org/10.1016/j.jclepro.2021.128933 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 321 2021 25 1025 0 |
| spelling |
10.1016/j.jclepro.2021.128933 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica (DE-627)ELV055384382 (ELSEVIER)S0959-6526(21)03126-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Wei, Wei verfasserin aut How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations Elsevier Zhang, Xueyuan oth Zhou, Liang oth Xie, Binbin oth Zhou, Junju oth Li, Chuanhua oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:321 year:2021 day:25 month:10 pages:0 https://doi.org/10.1016/j.jclepro.2021.128933 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 321 2021 25 1025 0 |
| allfields_unstemmed |
10.1016/j.jclepro.2021.128933 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica (DE-627)ELV055384382 (ELSEVIER)S0959-6526(21)03126-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Wei, Wei verfasserin aut How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations Elsevier Zhang, Xueyuan oth Zhou, Liang oth Xie, Binbin oth Zhou, Junju oth Li, Chuanhua oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:321 year:2021 day:25 month:10 pages:0 https://doi.org/10.1016/j.jclepro.2021.128933 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 321 2021 25 1025 0 |
| allfieldsGer |
10.1016/j.jclepro.2021.128933 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica (DE-627)ELV055384382 (ELSEVIER)S0959-6526(21)03126-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Wei, Wei verfasserin aut How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations Elsevier Zhang, Xueyuan oth Zhou, Liang oth Xie, Binbin oth Zhou, Junju oth Li, Chuanhua oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:321 year:2021 day:25 month:10 pages:0 https://doi.org/10.1016/j.jclepro.2021.128933 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 321 2021 25 1025 0 |
| allfieldsSound |
10.1016/j.jclepro.2021.128933 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica (DE-627)ELV055384382 (ELSEVIER)S0959-6526(21)03126-7 DE-627 ger DE-627 rakwb eng 540 VZ 35.18 bkl Wei, Wei verfasserin aut How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations Elsevier Zhang, Xueyuan oth Zhou, Liang oth Xie, Binbin oth Zhou, Junju oth Li, Chuanhua oth Enthalten in Elsevier Science Rajendiran, Rajmohan ELSEVIER Self-assembled 3D hierarchical MnCO 2020 Amsterdam [u.a.] (DE-627)ELV003750353 volume:321 year:2021 day:25 month:10 pages:0 https://doi.org/10.1016/j.jclepro.2021.128933 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 35.18 Kolloidchemie Grenzflächenchemie VZ AR 321 2021 25 1025 0 |
| language |
English |
| source |
Enthalten in Self-assembled 3D hierarchical MnCO Amsterdam [u.a.] volume:321 year:2021 day:25 month:10 pages:0 |
| sourceStr |
Enthalten in Self-assembled 3D hierarchical MnCO Amsterdam [u.a.] volume:321 year:2021 day:25 month:10 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Kolloidchemie Grenzflächenchemie |
| institution |
findex.gbv.de |
| topic_facet |
Nighttime light data Impact factors Mainland China CO2 emissions Spatiotemporal variations |
| dewey-raw |
540 |
| isfreeaccess_bool |
false |
| container_title |
Self-assembled 3D hierarchical MnCO |
| authorswithroles_txt_mv |
Wei, Wei @@aut@@ Zhang, Xueyuan @@oth@@ Zhou, Liang @@oth@@ Xie, Binbin @@oth@@ Zhou, Junju @@oth@@ Li, Chuanhua @@oth@@ |
| publishDateDaySort_date |
2021-01-25T00:00:00Z |
| hierarchy_top_id |
ELV003750353 |
| dewey-sort |
3540 |
| id |
ELV055384382 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055384382</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041611.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jclepro.2021.128933</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055384382</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0959-6526(21)03126-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.18</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wei, Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nighttime light data</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Impact factors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Mainland China</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CO2 emissions</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Spatiotemporal variations</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xueyuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Liang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Binbin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Junju</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Chuanhua</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Rajendiran, Rajmohan ELSEVIER</subfield><subfield code="t">Self-assembled 3D hierarchical MnCO</subfield><subfield code="d">2020</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV003750353</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:321</subfield><subfield code="g">year:2021</subfield><subfield code="g">day:25</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jclepro.2021.128933</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.18</subfield><subfield code="j">Kolloidchemie</subfield><subfield code="j">Grenzflächenchemie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">321</subfield><subfield code="j">2021</subfield><subfield code="b">25</subfield><subfield code="c">1025</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Wei, Wei |
| spellingShingle |
Wei, Wei ddc 540 bkl 35.18 Elsevier Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method |
| authorStr |
Wei, Wei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV003750353 |
| format |
electronic Article |
| dewey-ones |
540 - Chemistry & allied sciences |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
540 VZ 35.18 bkl How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations Elsevier |
| topic |
ddc 540 bkl 35.18 Elsevier Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations |
| topic_unstemmed |
ddc 540 bkl 35.18 Elsevier Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations |
| topic_browse |
ddc 540 bkl 35.18 Elsevier Nighttime light data Elsevier Impact factors Elsevier Mainland China Elsevier CO2 emissions Elsevier Spatiotemporal variations |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
x z xz l z lz b x bx j z jz c l cl |
| hierarchy_parent_title |
Self-assembled 3D hierarchical MnCO |
| hierarchy_parent_id |
ELV003750353 |
| dewey-tens |
540 - Chemistry |
| hierarchy_top_title |
Self-assembled 3D hierarchical MnCO |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV003750353 |
| title |
How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method |
| ctrlnum |
(DE-627)ELV055384382 (ELSEVIER)S0959-6526(21)03126-7 |
| title_full |
How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method |
| author_sort |
Wei, Wei |
| journal |
Self-assembled 3D hierarchical MnCO |
| journalStr |
Self-assembled 3D hierarchical MnCO |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Wei, Wei |
| container_volume |
321 |
| class |
540 VZ 35.18 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Wei, Wei |
| doi_str_mv |
10.1016/j.jclepro.2021.128933 |
| dewey-full |
540 |
| title_sort |
how does spatiotemporal variations and impact factors in co2 emissions differ across cities in china? investigation on grid scale and geographic detection method |
| title_auth |
How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method |
| abstract |
Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. |
| abstractGer |
Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. |
| abstract_unstemmed |
Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method |
| url |
https://doi.org/10.1016/j.jclepro.2021.128933 |
| remote_bool |
true |
| author2 |
Zhang, Xueyuan Zhou, Liang Xie, Binbin Zhou, Junju Li, Chuanhua |
| author2Str |
Zhang, Xueyuan Zhou, Liang Xie, Binbin Zhou, Junju Li, Chuanhua |
| ppnlink |
ELV003750353 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth |
| doi_str |
10.1016/j.jclepro.2021.128933 |
| up_date |
2024-07-06T17:24:05.415Z |
| _version_ |
1803851289504776192 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV055384382</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626041611.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220105s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jclepro.2021.128933</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001535.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV055384382</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0959-6526(21)03126-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.18</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wei, Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">How does spatiotemporal variations and impact factors in CO2 emissions differ across cities in China? Investigation on grid scale and geographic detection method</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Timely and accurate assessments of the spatiotemporal variations and impact factors of CO2 emissions are crucial for developing effective and rational CO2 reduction policies. However, studies are still lacking that consider spatiotemporal variations in CO2 emissions at the grid scale and their impact factors in cities with different types of such emissions. This study proposes an improved estimation model to map the CO2 emissions of mainland China in 2000, 2005, 2010, 2015 and 2018 based on nighttime light data, land use data, and population data. The resulting maps of estimated CO2 emissions in different regions are examined and compared with Landsat data and with the results of linear regression, quadratic polynomial carbon emission models, carbon emission models based on land use and carbon emission models based on population. This comparison shows that the model proposed in this study is more accurate than ordinary models, such as the linear regression and quadratic polynomial models. Using the results of this model, 356 mainland cities in China are divided into service-oriented, industrial, agricultural and comprehensive cities, and the impact factors of each city are analyzed. The results show that China's CO2 emissions are still concentrated in its smaller regions, but the intensity of this concentration is gradually decreasing. The spatial distribution characteristics of the carbon emissions of cities have an obvious circle-layer effect; however, the CO2 emissions of large cities present a “W”-shaped distribution, while the CO2 emissions of small cities present a power function curve. Although GDP is the main factor affecting the CO2 emissions in various cities, the factors affecting different types of cities based on their CO2 emissions vary greatly. These results could improve the current understanding of the patterns of variation and influencing factors of CO2 emissions in different cities and provide a scientific basis for formulating CO2 emission reduction policies in accordance with local conditions.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nighttime light data</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Impact factors</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Mainland China</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">CO2 emissions</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Spatiotemporal variations</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xueyuan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Liang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Binbin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Junju</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Chuanhua</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Rajendiran, Rajmohan ELSEVIER</subfield><subfield code="t">Self-assembled 3D hierarchical MnCO</subfield><subfield code="d">2020</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV003750353</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:321</subfield><subfield code="g">year:2021</subfield><subfield code="g">day:25</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jclepro.2021.128933</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.18</subfield><subfield code="j">Kolloidchemie</subfield><subfield code="j">Grenzflächenchemie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">321</subfield><subfield code="j">2021</subfield><subfield code="b">25</subfield><subfield code="c">1025</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.3999157 |