# Energy Use and Its Key Factors in Hotel Chains

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}adjusted equal to 0.9506, while the net profit per room (NetRoom) presented a negative relationship in both models, −0.0006 and −0.0010, respectively, with R

^{2}adjusted equal to 0.9304 in the second model. Investing in updating their energy systems, hotel chains can contribute to a more sustainable future, build positive marketing, retain guests, and generate a long-run financial return. This research contributes to the scientific literature by confirming relationships and providing evidence among new, and not yet explored, variables. It is expected to create a reference for policies to reduce energy use in hotels and for hotel owners to upgrade their systems.

## 1. Introduction

## 2. Literature Review

#### 2.1. Energy Use in Hotels

#### 2.2. GRI Adoption

## 3. Materials and Methods

#### 3.1. Data Collection

#### 3.2. Multiple Regression Model

_{0}is the intercept and ϵ is the error value. The expected value of the dependent parametric variable with error term is assumed to be zero, so the estimated multiple regression is obtained from Equation (3), where b

_{0}, b

_{1}, b

_{2}… b

_{n}are estimates of β

_{0}, β

_{1}, β

_{2}, … β

_{n}, and $\hat{\mathrm{Y}}$ is the predicted value for the dependent variable [22].

^{2}) is used [19,24]. Equation (4) presents the formula for R

^{2}calculation.

^{2}. This coefficient increases as new independent variables are added to the model, but it is not used to verify the model acceptance when more than one predictor variable is used. The solution for this is the use of the adjusted R

^{2}, which weighs more heavily on the model when there are more predictors. It penalizes the model for having too many predictors [23]. Equation (5) shows the adjusted R

^{2}(where p is the number of parameters and n is the number of observations).

## 4. Results

#### 4.1. Dispersion Matrix

#### 4.2. Multiple Regression—First Model

^{2}= 0.9551, and the adjusted coefficient of determination, R

^{2}adjusted = 0.9506, are calculated. To predict the energy values in hotels, Equation (6) is used, and all regression coefficients, including the intercept, showed statistical significance, with the p-value < 0.05.

^{2}and adjusted R

^{2}), it can be identified that the model represents the sample and can represent the population at a 5% confidence interval level. This is an important outcome because it connects a representative number of hotels distributed worldwide. Table 3 reports the Model 1 regression summary and the analysis of variance.

#### 4.3. Multiple Regression—Second Model

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Yi | Net Profit | RevPAR | Energy | Water | Carbon |
---|---|---|---|---|---|

H.1 | 22,878.30 | 444.28 | 331.34 | 2.35 | 139.44 |

H.2 | 5774.09 | 276.00 | 225.35 | 0.95 | 84.73 |

H.3 | 921.85 | 134.60 | 50.15 | 0.70 | 31.28 |

H.4 | 4899.54 | 49.85 | 27.66 | 0.24 | 14.49 |

H.5 | 6432.72 | 102.83 | 42.24 | 0.59 | 22.04 |

H.6 | 713.02 | 154.00 | 43.79 | 0.58 | 26.60 |

H.7 | 916.79 | 75.00 | 24.63 | 0.52 | 18.60 |

H.8 | 1135.02 | 79.38 | 11.83 | 0.55 | 19.00 |

H.9 | 3111.59 | 64.72 | 30.23 | 0.22 | 14.29 |

H.10 | 1699.31 | 81.78 | 23.83 | 0.54 | 16.10 |

H.11 | 3785.45 | 28.57 | 14.87 | 0.31 | 12.40 |

H.12 | 909.50 | 41.64 | 72.22 | 0.84 | 48.40 |

H.13 | 372.36 | 227.95 | 385.83 | 4.05 | 127.20 |

H.14 | 1253.55 | 41.11 | 40.41 | 0.92 | 29.81 |

H.15 | 3379.30 | 183.00 | 101.22 | 0.60 | 36.74 |

H.16 | 9510.91 | 183.46 | 89.29 | 0.79 | 27.33 |

H.17 | 15,864.20 | 146.00 | 22.45 | 0.15 | 9.31 |

H.18 | 911.73 | 109.65 | 69.52 | 0.14 | 23.97 |

H.19 | 303.39 | 85.41 | 84.30 | 0.51 | 23.81 |

H.20 | 1828.67 | 67.77 | 48.38 | 0.33 | 8.42 |

H.21 | 1504.88 | 80.90 | 53.18 | 0.18 | 1.96 |

H.22 | 5730.57 | 144.51 | 62.65 | 0.50 | 19.83 |

H.23 | 20,119.99 | 183.60 | 100.64 | 0.75 | 29.06 |

H.24 | 4160.77 | 120.09 | 234.29 | 1.58 | 80.20 |

H.25 | 238,718.90 | 266.70 | 362.50 | 2.60 | 260.27 |

H.26 | 1863.48 | 101.61 | 108.66 | 1.08 | 32.30 |

H.27 | −47,272.73 | 166.50 | 163.99 | 2.56 | 67.22 |

H.28 | 471.24 | 30.71 | 42.58 | 0.24 | 11.41 |

H.29 | 8238.21 | 210.65 | 80.46 | 0.51 | 19.89 |

H.30 | 8524.15 | 101.85 | 9.29 | 0.26 | 3.69 |

H.31 | 12,614.36 | 122.98 | 47.83 | 0.67 | 29.00 |

H.32 | 13,247.55 | 808.62 | 636.18 | 0.43 | 274.83 |

H.33 | 2762.43 | 69.85 | 120.00 | 2.41 | 38.90 |

H.34 | 3955.69 | 217.42 | 53.17 | 0.65 | 14.41 |

H.35 | −3979.84 | 104.27 | 28.31 | 0.32 | 7.85 |

H.36 | 4311.40 | 148.00 | 40.37 | 0.54 | 9.47 |

H.37 | 1614.62 | 37.41 | 17.71 | 0.44 | 19.28 |

H.38 | 49,037.36 | 153.00 | 167.76 | 1.30 | 64.57 |

H.39 | 4398.52 | 58.46 | 121.44 | 2.14 | 35.60 |

H.40 | 121.75 | 54.43 | 51.11 | 2.19 | 21.70 |

H.41 | 4564.21 | 145.34 | 22.80 | 0.37 | 11.14 |

H.42 | −587.73 | 82.26 | 118.16 | 0.89 | 48.80 |

H.43 | 2664.17 | 52.16 | 17.29 | 0.35 | 8.57 |

H.44 | 13,458.34 | 196.08 | 66.95 | 0.57 | 18.31 |

H.45 | 5124.64 | 115.84 | 82.27 | 0.67 | 70.23 |

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**Figure 2.**Linearity and normality tests for Model 1. (

**a**) Phenomenon linearity; (

**b**) residuals’ normality.

**Figure 4.**Linearity, residual normality, and homoscedasticity tests for Model 2. (

**a**) Phenomenon linearity; (

**b**) residuals’ normality; (

**c**) homoscedasticity.

Authors | Sample and Period | Region | Dependent Variable(s) | Independent Variable(s) | Significant Result(s) |
---|---|---|---|---|---|

[2] | 29 hotels—2004 | Singapore | Energy use | Star rating Worker density Last retrofit | Star rating (−) Worker density (+) |

[3] | 30 hotels—2013 | Hong Kong | Energy use | Building age Floor area Guestroom Occupancy rate Maintenance costs | Floor area (+) |

[4] | 6 hotels—2019/2020 | Gran Canaria | Energy use | Overnight stays Number of diners Number of workers Number of rooms RevPAR Pool volume Number of guests per room | RevPAR (+) Number of diners (+) Pool volume (+) Number of guests per room (+) |

[9] | 73 hotels—2010 | Taiwan | Energy use | Foreign individual travelers (FIT) Number of group guests | FIT (+) Group guests (+) |

[12] | 55 hotels—2018 | Tunisia | Energy use and water use | Floor area Number of beds Number of guests rooms Number of guests-nights Occupancy rate Floor area (guestrooms) | Floor area (guestrooms) (+) Number of guests-nights (+) |

[4] | 6 hotels—2019/2020 | Gran Canaria | Energy use | Overnight stays Number of diners Number of workers Number of rooms RevPAR Pool volume Number of guests per room | RevPAR (+) Number of diners (+) Pool volume (+) Number of guests per room (+) |

[13] | 184 hotels—2004 | Europe | Energy and water use | Floor area Guest-nights sold Food cover sold laundry washed on-site On-site health club | Floor area (+) Guest-nights sold (+) Food cover sold (+) |

[14] | 200 hotels—2010 | Taiwan | Energy use | Floor area Number of rooms Number of buildings Number of workers Occupancy rate ADR Total revenue Number of guests | Floor area (+) Number of rooms (+) ADR (+) Total revenue (+) Occupancy rate (+) |

[15] | 24 hotels—2013 | Lijiang, China | Energy use | Floor area Number of guests rooms Star rating Occupancy rate Room revenue Number of workers Floor area (guestrooms) | Star rating (+) Occupancy rate (+) |

Variable | Valid n | Mean | Minimum | Maximum | Std. Deviation |
---|---|---|---|---|---|

NetRoom (USD) | 45 | 9821.5 | −47,272.7 | 238,718.9 | 36,788.6 |

RevPAR (USD) | 45 | 141.1 | 28.6 | 808.6 | 129.0 |

Energy (KWh/occupied room) | 45 | 101.0 | 9.3 | 636.2 | 121.3 |

Water (m^{3}/occupied room) | 45 | 0.9 | 0.1 | 4.0 | 0.8 |

Carbon (kgCO2e/occupied room) | 45 | 42.9 | 2.0 | 274.8 | 57.2 |

Panel A—Regression Summary | |||

Variable | Coefficient | t-Statistic | p-Value |

Intercept | −17.8615 | −2.2049 | 0.033 |

NetRoom | −0.0006 | −3.8490 | <0.001 |

RevPAR | 0.2437 | 3.7953 | <0.001 |

Water | 23.6850 | 3.7234 | <0.001 |

Carbon | 1.6201 | 8.3709 | <0.001 |

Panel B—Analysis of Variance | |||

F-Statistic | 212.86 | ||

p-value | <0.001 | ||

R^{2} Adjusted | 0.9506 |

Panel A—Regression Summary | |||

Variable | Coefficient | t-Statistic | p-Value |

Intercept | 9.6675 | 1.5907 | 0.119 |

NetRoom | −0.0010 | −5.9775 | <0.001 |

Carbon | 2.3502 | 22.5844 | <0.001 |

Panel B—Analysis of Variance | |||

F-Statistic | 295.10 | ||

p-value | <0.001 | ||

R^{2} Adjusted | 0.9304 |

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## Share and Cite

**MDPI and ACS Style**

Arenhart, R.S.; Souza, A.M.; Zanini, R.R.
Energy Use and Its Key Factors in Hotel Chains. *Sustainability* **2022**, *14*, 8239.
https://doi.org/10.3390/su14148239

**AMA Style**

Arenhart RS, Souza AM, Zanini RR.
Energy Use and Its Key Factors in Hotel Chains. *Sustainability*. 2022; 14(14):8239.
https://doi.org/10.3390/su14148239

**Chicago/Turabian Style**

Arenhart, Rodrigo Schons, Adriano Mendonça Souza, and Roselaine Ruviaro Zanini.
2022. "Energy Use and Its Key Factors in Hotel Chains" *Sustainability* 14, no. 14: 8239.
https://doi.org/10.3390/su14148239