Built structures influence patterns of energy demand and CO2 emissions across countries

被引:17
|
作者
Haberl, Helmut [1 ]
Loew, Markus [1 ]
Perez-Laborda, Alejandro [2 ,3 ]
Matej, Sarah [1 ]
Plank, Barbara [1 ]
Wiedenhofer, Dominik [1 ]
Creutzig, Felix [4 ,5 ]
Erb, Karl-Heinz [1 ]
Duro, Juan Antonio [2 ,3 ]
机构
[1] Univ Nat Resources & Life Sci, Inst Social Ecol, Vienna, Austria
[2] Univ Rovira i Virgili, Econ Dept & Ecosos, Tarragona, Spain
[3] Univ Rovira i Virgili, Eco SOS, Tarragona, Spain
[4] Mercator Res Inst Global Commons & Climate Change, EUREF 19, D-10829 Berlin, Germany
[5] Tech Univ Berlin, Str 17 Junis 135, D-10623 Berlin, Germany
基金
欧洲研究理事会;
关键词
MATERIAL STOCKS; SUSTAINABILITY; URBANIZATION; POPULATION; TRANSPORT; CONSUMPTION; REGRESSION; DENSITY; CITIES; IMPACT;
D O I
10.1038/s41467-023-39728-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Extent and spatial patterns of settlements and infrastructures strongly affect resource demand of national economies worldwide. Their influence on final energy and CO2 emissions is almost as large as that of gross domestic product (GDP). Built structures, i.e. the patterns of settlements and transport infrastructures, are known to influence per-capita energy demand and CO2 emissions at the urban level. At the national level, the role of built structures is seldom considered due to poor data availability. Instead, other potential determinants of energy demand and CO2 emissions, primarily GDP, are more frequently assessed. We present a set of national-level indicators to characterize patterns of built structures. We quantify these indicators for 113 countries and statistically analyze the results along with final energy use and territorial CO2 emissions, as well as factors commonly included in national-level analyses of determinants of energy use and emissions. We find that these indicators are about equally important for predicting energy demand and CO2 emissions as GDP and other conventional factors. The area of built-up land per capita is the most important predictor, second only to the effect of GDP.
引用
收藏
页数:10
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