Editorial Feature

Analysis of the Effect of Vegetation Coverage on Urban Energy Consumption

The most intuitive spatial aspect of urbanization is that man-made space replaces natural space, resulting in an urban heat island that threatens human thermal comfort, health, and energy usage. Several investigations have examined the impact of urban vegetation coverage on the thermal environment, but the issue of how that impact will affect urban energy consumption needs has been overlooked. This has been explored in the journal Buildings.

Study: Study on the Effect of Vegetation Coverage on Urban Cooling and Energy Conservation: A Case Study of a Typical Hilly City, Chenzhou, China. Image Credit: Teo Tarras/Shutterstock.com

Currently, research on the impact of urban vegetation coverage on the thermal environment is mostly carried out through three methods. To begin, field measurement is often employed. Second, remote sensing is employed to investigate the thermal effects of large-scale greening. The simulation method is utilized as the third method to evaluate the effects of greening on the micro-scale thermal environment.

Although numerous researchers have looked at the impact of urban greening on the thermal environment, only a few have looked at the impact of greening on conserving energy at the micro-scale. Unfortunately, no research has been done on the impact of general urban vegetation covering on overall city energy savings, which is the novelty of this work.


The connection between vegetation coverage and energy conservation was investigated in four steps. The impact of vegetation cover on land surface temperature was first discovered. The research collected the relevant information on vegetation coverage and land surface temperature using the remote sensing inversion method, which was then evaluated using correlation and regression techniques.

Secondly, the literature on temperature and energy usage was studied, and the relationship between the two was established. Finally, using the three stages outlined above, this article calculated the impact of vegetation covering on urban energy conservation. Figure 1 depicts the abstract image of this investigation.

Abstract picture of the study.

Figure 1. Abstract picture of the study. Image Credit: Luo, et al., 2022

This report estimated the atmospheric correction parameter, as shown in Figure 2, using the NASA official website.

Atmospheric correction parameter calculation.

Figure 2. Atmospheric correction parameter calculation. Image Credit: Luo, et al., 2022


Chenzhou was chosen as the study location since it is a typical hilly city with a diverse vegetation cover. Chenzhou is split into 26 districts, and Figure 3 depicts the layout of these.

The 26 areas with urbanization characteristics.

Figure 3. The 26 areas with urbanization characteristics. Image Credit: Luo, et al., 2022

The vegetation coverage of 26 regulatory proper planning districts in Chenzhou was extracted independently using Envi software, as shown in Figure 4, where the legend represents the vegetation coverage value between 0 and 1.

Figure 5 depicts the average surface temperatures of the 26 districts, with the legend indicating the land surface temperature value between 0 and 1.

Overall map of vegetation coverage in 26 areas.

Figure 4. Overall map of vegetation coverage in 26 areas. Image Credit: Luo, et al., 2022

Overall map of land surface temperatures in 26 areas.

Figure 5. Overall map of land surface temperatures in 26 areas. Image Credit: Luo, et al., 2022

Table 1 shows the overall land surface temperature (ST) and vegetation coverage (VC) of the 26 districts.

Table 1. Statistical table of mean temperature and vegetation coverage. Source: Luo, et al., 2022

No ST (°C) VC No ST (°C) VC No ST (°C) VC
1 32.2273 0.4463 10 36.3274 0.1396 19 32.5020 0.4506
2 34.8395 0.1774 11 35.3807 0.1281 20 33.0571 0.3688
3 34.7336 0.2951 12 35.0846 0.1284 21 31.5379 0.4849
4 34.6917 0.1893 13 33.9371 0.2618 22 33.6529 0.2697
5 35.5906 0.1842 14 33.2060 0.3292 23 33.8655 0.3906
6 35.7338 0.1306 15 34.1990 0.3018 24 33.8203 0.4010
7 32.1797 0.4612 16 31.3097 0.5457 25 34.0082 0.3836
8 33.3703 0.2960 17 33.8162 0.3398 26 33.0308 0.2034
9 35.4977 0.1600 18 32.0618 0.4616      


The Pearson correlation approach was then used to examine the relationship between surface temperature and vegetation coverage in these 26 districts to see if there was an intrinsic relationship.

Table 2 shows the findings of the Pearson correlation analysis.

Table 2. Descriptive statistics and correlations. Source: Luo, et al., 2022

Descriptive Statistics
  Mean Std. Deviation N
Vegetation coverage 0.30495881 0.127784197 26
Temperature 33.83313469 1.358457073 26
    Vegetation coverage Temperature
Vegetation coverage Pearson Correlation 1 -0.892
Sig.(2-tailed)   0
N 26 26
Temperature Pearson Correlation -0.892 1
Sig.(2-tailed) 0  
N 26 26


Understanding the influence of vegetation coverage on surface temperature is crucial to further investigating the effects of vegetation coverage on energy consumption.

As a result, these 26 groups of information were subjected to linear model regression analysis, quadratic model regression analysis, compound model regression analysis, logarithmic model regression analysis, cubic function model regression analysis, S model regression analysis, inverse model regression analysis, power model regression analysis, exponential distribution model regression analysis, and growth model regression analysis.

Figure 6 depicts the outcomes of their research.

Fitting and optimum selection of regression function.

Figure 6. Fitting and optimum selection of regression function. Image Credit: Luo, et al., 2022

In Figure 6, both the inverse model and the S function model fit the point distribution well. When examining the connection between density and urban functions in urban planning, it is only permitted in urban industrial districts when the vegetation density is less than 20%, and other functional departments are not permitted.

There is a deviating point in Figure 6 that has been highlighted in red. The deviating point indicates that foliage coverage and surface temperature were both low, contradicting the research findings.

Several statistical types of research, particularly in the energy disciplines, have investigated the effects of temperature on energy usage, with numerous positive outcomes. There are some notable academic findings on the impact of temperature on energy use.

Equation 3 shows the function expression, whereas Figure 7 shows the related function image.

? and T model image.

Figure 7. λ and T model image. Image Credit: Luo, et al., 2022

Figure 7 indicates that while the temperature is below 22°C, air conditioning energy usage is unaffected by temperature changes.


The findings of this research can anticipate the city’s entire energy demand based on vegetation coverage, which is important in urban planning. Moreover, some study limitations need to be addressed:

(1) Although the land surface temperature and the air temperature around it have a strong relationship, the temperature in the “λ-T” model is not the land surface temperature, which may diminish the precision of the result. The subject of land surface temperature and air temperature around the land surface will be investigated further.

(2) This research focused on 26 different districts throughout the city. In the follow-up investigation, more samples will be examined to ensure that the conclusions are universal.

Journal Reference:

Luo, X., Wang, J., Li, J. (2022) Study on the Effect of Vegetation Coverage on Urban Cooling and Energy Conservation: A Case Study of a Typical Hilly City, Chenzhou, China. Buildings, 12(5), p. 640. Available Online: https://www.mdpi.com/2075-5309/12/5/640/htm

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