Impact of urbanization and climate warming on energy consumption in large cities

Клименко, В. В.; Гинзбург, А. С.; Demchenko, P. F.; Tereshin, A. G.; Belova, I. N.; Касилова, Е. В.

doi:10.1134/s1028335816100050

Cited by 28 publications

(9 citation statements)

References 9 publications

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“…This phenomenon worsens every year and is more pronounced at night [ 23 , 24 ]. As for the formation mechanism, more attention are paid to land use/land cover (LULC), landscape index, and energy consumption [ 25 , 26 , 27 ]. The correlation between soil wetness, soil brightness, and LST [ 28 , 29 ] has been demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of a Spatial Distribution and Driving Factors of the Urban Heat Island Effect: A Case Study of Fuzhou Central Area, China

You

Lai

Lin

et al. 2021

IJERPH

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Land surface temperature (LST) is a joint product of physical geography and socio-economics. It is important to clarify the spatial heterogeneity and binding factors of the LST for mitigating the surface heat island effect (SUHI). In this study, the spatial pattern of UHI in Fuzhou central area, China, was elucidated by Moran’s I and hot-spot analysis. In addition, the study divided the drivers into two categories, including physical geographic factors (soil wetness, soil brightness, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), water density, and vegetation density) and socio-economic factors (normalized difference built-up index (NDBI), population density, road density, nighttime light, park density). The influence analysis of single factor on LST and the factor interaction analysis were conducted via Geodetector software. The results indicated that the LST presented a gradient layer structure with high temperature in the southeast and low temperature in the northwest, which had a significant spatial association with industry zones. Especially, LST was spatially repulsive to urban green space and water body. Furthermore, the four factors with the greatest influence (q-Value) on LST were soil moisture (influence = 0.792) > NDBI (influence = 0.732) > MNDWI (influence = 0.618) > NDVI (influence = 0.604). The superposition explanation degree (influence (Xi ∩ Xj)) is stronger than the independent explanation degree (influence (Xi)). The highest and the lowest interaction existed in ”soil wetness ∩ MNDWI” (influence = 0.864) and “nighttime light ∩ population density” (influence = 0.273), respectively. The spatial distribution of SUHI and its driving mechanism were also demonstrated, providing theoretical guidance for urban planners to build thermal environment friendly cities.

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Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of a Spatial Distribution and Driving Factors of the Urban Heat Island Effect: A Case Study of Fuzhou Central Area, China

You

Lai

Lin

et al. 2021

IJERPH

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“…GRP per capita characterizes the level of socio-economic development. Analysis of the indicator allows us to conclude that there is a significant right-sided distribution asymmetry (5,259), which indicates that in most regions there is an excess of the average Russian results for this indicator ( peaked character of the distribution, which means the concentration of regions in the area of average Russian consumption, as well as the presence of anomalous values. There is a discrepancy between the quartile distance and the standard deviation.…”

Section: Resultsmentioning

confidence: 99%

“…Among the authors studying economic security in the energy sector we single out V.V. Klimenko [5], M.F. Keating [6], A. Mottaeva [7], I. Bashmakov [8].…”

Section: Introductionmentioning

confidence: 99%

Impact of energy economy development on the region’s population life quality

et al. 2019

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Regional economic development depends on development of fuel and energy industry facilities capable of ensuring a stable and reliable supply of energy carriers. At present, it is impossible to imagine livelihood of population without electrical energy. Russia produces 1094 billion kWh of electrical energy, of which 580 billion kWh is consumed by industry. Electrical energy is the basis for development of all types of economic activities. It is a condition for creation of material values and an increase in the standard of living of population. Modern industrial technologies are characterized by high energy intensity. Industry is dependent on electrical energy which drives power equipment. Electric power affects productivity which is the main indicator of production efficiency. The increase in GRP is mainly accounted for manufacturing industry (high-tech production) and service sector. Such indicators as GRP per capita, the volume of goods shipped by manufacturing industries, consumption of electricity by industry, and electric power per worker characterize sustainable socio-economic development of regions. It is relevant to conduct a statistical analysis of these indicators and identify development prospects to ensure energy security of regions.

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“…To the best of our knowledge, only Klimenko, et al [84] compared the differences in the HDDs and CDDs between the urban and suburban stations for several Russian cities.…”

Section: Impacts Of Urbanization On Degree Daysmentioning

confidence: 99%

“…To clarify the impacts of urbanization on degree days (Figure 3), existing studies usually simply compare the differences in the HDDs or CDDs calculated based on daily temperature data between urban and suburban or rural stations of a single city [41,[81][82][83]. To the best of our knowledge, only Klimenko, et al [84] compared the differences in the HDDs and CDDs between the urban and suburban stations for several Russian cities. Existing studies have usually ignored the differences in the influence of urbanization influence on the degree days within cities, among different cities, between the daytime and nighttime, and among different seasons or years, as well as the effects of relative humidity, population, and other factors.…”

Section: Impacts Of Urbanization On Degree Daysmentioning

confidence: 99%

A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

Wang

et al. 2020

IJERPH

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The world is faced with significant climate change, rapid urbanization, massive energy consumption, and tremendous pressure to reduce greenhouse gases. Building heating and cooling is one primary source of energy consumption and anthropogenic carbon dioxide emissions. First, this review presents previous studies that estimate the specific amount of climate change impact on building heating and cooling energy consumption, using the statistical method, physical model method, comprehensive assessment model method, and the combination method of statistical and physical model methods. Then, because the heating and cooling degree days indices can simply and reliably indicate the effects of climate on building heating and cooling energy consumption, previous studies were reviewed from the aspects of heating and cooling degree days indices, regional spatial-temporal variations in degree days and related indices, influencing factors of the spatial distributions of degree days, and the impacts of urbanization on degree days. Finally, several potential key issues or research directions were presented according to the research gaps or fields that need to be studied further in the future, such as developing methods to simply and accurately estimate the specified amounts of climate change impact on building cooling and heating energy consumption; using more effective methods to analyze the daytime, nighttime, and all-day spatial-temporal changes in different seasons in the past and future under various environment contexts by considering not only the air temperature but also the relative humidity, solar radiation, population, etc., and further exploring the corresponding more kinds of driving forces, including the various remotely sensed indices, albedo, nighttime light intensity, etc.; estimating the daytime, nighttime, and all-day impacts of urbanization on heating degree days (HDDs), cooling degree days (CDDs), and their sum (HDDs + CDDs) for vast cities in different environmental contexts at the station site, city, regional and global scales; producing and sharing of the related datasets; and analyzing the subsequent effects induced by climate change on the energy consumption for building heating and cooling, etc.

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Impact of urbanization and climate warming on energy consumption in large cities

Cited by 28 publications

References 9 publications

Quantitative Analysis of a Spatial Distribution and Driving Factors of the Urban Heat Island Effect: A Case Study of Fuzhou Central Area, China

Quantitative Analysis of a Spatial Distribution and Driving Factors of the Urban Heat Island Effect: A Case Study of Fuzhou Central Area, China

Impact of energy economy development on the region’s population life quality

A Review of Studies Involving the Effects of Climate Change on the Energy Consumption for Building Heating and Cooling

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