# Which Influencing Factors Could Reduce Ecological Consumption? Evidence from 90 Countries for the Time Period 1996–2015

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Ecological Footprint and Levels of Ecological Consumption

_{consumption}= EF

_{production}+ EF

_{imports}− EF

_{exports}) [16]. Therefore, the land and water to be calculated are not only within national borders but also outside national borders. Because EF values vary greatly with consumption behaviors and habits, it is not difficult to understand global ecological impacts of individual daily lives with the use of the EF [17,18]. The EF is a biophysical rather than monetary accounting approach to measuring ecological consumption. The measuring unit of the EF is global hectares (gha) per capita. A global hectare represents an ecologically productive hectare with global average biological productivity.

## 3. Influencing Factors and Control Variables

#### 3.1. Urbanization

**Hypothesis**

**1**

**(H1).**

#### 3.2. Renewable Energy Consumption

**Hypothesis**

**2**

**(H2).**

#### 3.3. Service Industries

**Hypothesis**

**3**

**(H3).**

#### 3.4. Internet Usage

**Hypothesis**

**4**

**(H4).**

#### 3.5. Control Variables: Education and Income

## 4. Regression Variables, Data Sources, and Econometric Framework

^{2}were the independent variables. Ln(GNIPC) is the natural log form of GNIPC. Ln(GNIPC)

^{2}is the square of Ln(GNIPC). Because marginal impacts of income on ecological consumption are supposed to be diminished, Ln(GNIPC) rather than GNIPC is used in Equation (2). Relative to GNIPC, Ln(GNIPC) could minimize the potential estimation bias caused by extreme income values. $\alpha $ represents the intercept term. ${\beta}_{1},{\beta}_{2},{\beta}_{3},{\beta}_{4},{\beta}_{5},{\beta}_{6}$, and ${\beta}_{7}$ represent the slope coefficients of URB, REN, SER, INT, MYS, Ln(GNIPC), and Ln(GNIPC)

^{2}, respectively. $i$ represents the sample countries (cross-section), which indicates the country-specific effects. $t$ denotes the time period (years), which indicates the time series effects. ${\epsilon}_{i,t}$ is the stochastic error term, which captures the impacts of all unobserved variables on EF.

- This paper estimates Equation (2) by employing the country random effects model. The estimation is called Model (1);
- This paper estimates Equation (2) by employing the country fixed effects model. The estimation is called Model (2);
- Between the country random effects model and the country fixed effects model, this paper selects an appropriate model by employing the Hausman test;
- To minimize the potential estimation bias caused by heteroscedasticity, this paper estimates Equation (2) by employing the selected appropriate model and robust standard errors. Robust standard errors are clustered on the country. The estimation is called Model (3);
- To explore the lagged impacts of the independent variables on EF (lagged by one year), this paper estimates Equation (4) by employing the selected appropriate model and robust standard errors. Robust standard errors are clustered on the country. The estimation is called Model (4);
- If there is not an inversed U-shaped or U-shaped relationship between ecological consumption and income, this paper estimates Equation (3) by employing the selected appropriate model and robust standard errors. Robust standard errors are clustered on the country. The estimation is called Model (5);
- If there is not an inversed U-shaped or U-shaped relationship between ecological consumption and lagged income (lagged by one year), this paper estimates Equation (5) by employing the selected appropriate model and robust standard errors. Robust standard errors are clustered on the country. The estimation is called Model (6).

## 5. Regression Estimation Results

#### 5.1. All Sample Countries (90)

^{2}was significant. According to the arguments in Section 4, an inversed U-shaped or U-shaped relationship between ecological consumption and income could not be statistically validated. Therefore, a linear relationship was explored instead. The scatter plot of EF and GNIPC (Figure 3) further demonstrates that a linear relationship was more appropriate. We ought to interpret the estimated relationships between the independent variables and EF based on the Models (5) and (6).

#### 5.2. Developed Countries (42)

^{2}showed small differences. URB, REN, and SER had statistically significant and negative impacts on EF. Ln(GNIPC) and Ln(GNIPC)

^{2}had significantly positive and negative impacts on EF, respectively, which validated an inversed U-shaped relationship between ecological consumption and income. According to the estimated coefficients of Ln(GNIPC) and Ln(GNIPC)

^{2}in Model (3), the turning point value of GNIPC of the inversed U-shaped relationship was 39,014. The scatter plot of EF and GNIPC (Figure 4) further verifies the turning point. For the sample, there were about 15% of the observations with GNIPC values higher than the turning point. INT and MYS were statistically insignificant. The VIF values of INT and MYS were 3.09 and 2.02, respectively, which demonstrates that the estimations of INT and MYS were not likely affected by the problems with multicollinearity.

#### 5.3. Developing Countries (48)

^{2}showed small differences. REN, SER, and INT had statistically significant and negative impacts on EF. Ln(GNIPC) and Ln(GNIPC)

^{2}had significantly negative and positive impacts on EF, respectively, which validated a U-shaped relationship between ecological consumption and income. According to the estimated coefficients of Ln(GNIPC) and Ln(GNIPC)

^{2}in Model (3), the turning point value of GNIPC of the U-shaped relationship was 706. The scatter plot of EF and GNIPC (Figure 5) further certifies the U-shaped relationship and the turning point. For the sample, there were about 95% of the observations with GNIPC values higher than the turning point. URB and MYS were statistically insignificant. The VIF values of URB and MYS were 3.24 and 2.82, respectively, which demonstrates that the estimations of URB and MYS were not likely affected by the problems with multicollinearity.

## 6. Discussion and Conclusions

_{2}emissions in 31 OECD (Organization for Economic Cooperation and Development) countries for the time period 1991–2012 [42]. Therefore, “internet for ecological sustainability” should be a new guidance principle when developed countries design internet businesses and industries.

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

**Table A1.**Estimations of impacts of influencing factors and control variables for the two sub-samples (GNIPC values above/below median).

Sub-Sample (GNIPC Values above Median) | Sub-Sample (GNIPC Values below Median) | |||
---|---|---|---|---|

Model (3) | Model (4) | Model (3) | Model (4) | |

Coefficient (RSE) | Coefficient (RSE) | Coefficient (RSE) | Coefficient (RSE) | |

URB | −0.036 * (0.02) | −0.032 * (0.02) | −0.000 (0.01) | 0.001 (0.01) |

REN | −0.063 *** (0.01) | −0.048 *** (0.01) | −0.011 *** (0.00) | −0.009 *** (0.00) |

SER | −0.053 ** (0.02) | −0.059 *** (0.02) | −0.004 * (0.00) | −0.004 ** (0.00) |

INT | 0.001 (0.00) | 0.003 (0.00) | 0.001 (0.00) | −0.000 (0.00) |

MYS | 0.065 (0.07) | 0.049 (0.07) | -0.037 (0.03) | −0.023 (0.03) |

Ln(GNIPC) | 10.505 *** (3.74) | 12.745 *** (3.44) | −1.584 *** (0.55) | −1.812 *** (0.60) |

Ln(GNIPC)^{2} | −0.500 *** (0.19) | −0.638 *** (0.18) | 0.117 *** (0.04) | 0.132 *** (0.04) |

Constant | −43.766 ** (18.09) | −52.587 *** (16.70) | 7.733 *** (2.07) | 8.345 *** (2.27) |

Prob > F-statistic | 0.00 | 0.00 | 0.00 | 0.00 |

R-squared | 0.29 | 0.27 | 0.29 | 0.31 |

Obs | 877 | 907 | 851 | 822 |

Groups | 60 | 60 | 62 | 61 |

## References

- Daly, H.E. Some overlaps between the first and second thirty years of ecological economics. Ecol. Econ.
**2019**, 164, 106372. [Google Scholar] [CrossRef] - Polasky, S.; Kling, C.L.; Levin, S.A. Role of economics in analyzing the environment and sustainable development. Proc. Nati. Acad. Sci. USA
**2019**, 116, 5233–5238. [Google Scholar] [CrossRef] [PubMed][Green Version] - Sol, J. Economics in the anthropocene: Species extinction or steady state economics. Ecol. Econ.
**2019**, 165, 106392. [Google Scholar] [CrossRef][Green Version] - Rockström, J.; Steffen, W.; Noone, K.; Persson, Å.; Chapin, F.S., III; Lambin, E.F.; Lenton, T.M.; Scheffer, M.; Folke, C.; Schellnhuber, H.J.; et al. A safe operating space for humanity. Nature
**2009**, 461, 472–475. [Google Scholar] [CrossRef] - Steffen, W.; Richardson, K.; Rockström, J. Planetary boundaries: Guiding human development on a changing planet. Science
**2015**, 347, 1259855. [Google Scholar] [CrossRef][Green Version] - Freeman, R. A theory on the future of the rebound effect in a resource-constrained world. Front. Energy Res.
**2018**, 6, 81. [Google Scholar] [CrossRef][Green Version] - Galli, A.; Iha, K.; Halle, M.; Bilali, H.E.; Bottalico, F. Mediterranean countries’ food consumption and sourcing patterns: An Ecological Footprint viewpoint. Sci. Total Environ.
**2017**, 578, 383–391. [Google Scholar] [CrossRef][Green Version] - Wackernagel, M.; Hanscom, L.; Lin, D. Making the Sustainable Development Goals consistent with sustainability. Front. Energy Res.
**2017**, 5, 18. [Google Scholar] [CrossRef][Green Version] - Sahin, E.S.; Bayram, I.S.; Koc, M. Demand side management opportunities, framework, and implications for sustainable development in resource-rich countries: Case study Qatar. J. Clean. Prod.
**2019**, 241, 118332. [Google Scholar] [CrossRef] - Al-Mulali, U.; Ozturk, I.; Solarin, S.A. Investigating the environmental Kuznets curve hypothesis in seven regions: The role of renewable energy. Ecol. Indic.
**2016**, 67, 267–282. [Google Scholar] [CrossRef] - Klugman, J.; Rodríguez, F.; Choi, H.J. The HDI 2010: New controversies, old critiques. J. Econ. Inequal.
**2011**, 9, 249–288. [Google Scholar] [CrossRef] - Collins, A.; Galli, A.; Patrizi, N. Learning and teaching sustainability: The contribution of Ecological Footprint calculators. J. Clean. Prod.
**2018**, 174, 1000–1010. [Google Scholar] [CrossRef] - Mancini, M.S.; Galli, A.; Coscieme, L. Exploring ecosystem services assessment through Ecological Footprint accounting. Ecosyst. Serv.
**2018**, 30, 228–235. [Google Scholar] [CrossRef] - Jóhannesson, S.E.; Davíesdóttir, B.; Heinonen, J.T. Standard Ecological Footprint method for small, highly specialized economies. Ecol. Econ.
**2018**, 146, 370–380. [Google Scholar] [CrossRef] - Aşıcı, A.A.; Acar, S. Does income growth relocate ecological footprint? Ecol. Indic.
**2016**, 61, 707–714. [Google Scholar] [CrossRef] - Claborn, K.A.; Brooks, J.S. Can we consume less and gain more? Environmental efficiency of well-being at the individual level. Ecol. Econ.
**2019**, 156, 110–120. [Google Scholar] [CrossRef] - Aydin, C.; Esen, Ö.; Aydin, R. Is the ecological footprint related to the Kuznets curve a real process or rationalizing the ecological consequences of the affluence? Evidence from PSTR approach. Ecol. Indic.
**2019**, 98, 543–555. [Google Scholar] [CrossRef] - O’Neill, D.W.; Fanning, A.L.; Lamb, W.F.; Steinberger, J.K. A good life for all within planetary boundaries. Nat. Sustain.
**2018**, 1, 88–95. [Google Scholar] [CrossRef][Green Version] - Danish; Wang, Z.H. Investigation of the ecological footprint’s driving factors: What we learn from the experience of emerging economies. Sustain. Cities Soc.
**2019**, 49, 101626. [Google Scholar] [CrossRef] - Baloch, M.A.; Zhang, J.; Iqbal, K.; Iqbal, Z. The effect of financial development on ecological footprint in BRI countries: Evidence from panel data estimation. Environ. Sci. Pollut. Res.
**2019**, 26, 6199–6208. [Google Scholar] [CrossRef] - Wang, J.; Dong, K. What drives environmental degradation? Evidence from 14 Sub-Saharan African countries. Sci. Total Environ.
**2019**, 656, 165–173. [Google Scholar] [CrossRef] [PubMed] - Nathaniel, S.; Nwodo, O.; Adediran, A.; Sharma, G.; Shah, M.; Adeleye, N. Ecological footprint, urbanization, and energy consumption in South Africa: Including the excluded. Environ. Sci. Pollut. Res.
**2019**, 26, 27168–27179. [Google Scholar] [CrossRef] [PubMed] - Ahmed, Z.; Wang, Z. Investigating the impact of human capital on the ecological footprint in India: An empirical analysis. Environ. Sci. Pollut. Res.
**2019**, 26, 26782–26796. [Google Scholar] [CrossRef] [PubMed] - Dogan, E.; Taspinar, N.; Gokmenoglu, K.K. Determinants of ecological footprint in MINT countries. Energy Environ.
**2019**, 30, 1065–1086. [Google Scholar] [CrossRef] - Balsalobre-Lorente, D.; Gokmenoglu, K.K.; Taspinar, N.; Cantos-Cantos, J.M. An approach to the pollution haven and pollution halo hypotheses in MINT countries. Environ. Sci. Pollut. Res.
**2019**, 26, 23010–23026. [Google Scholar] [CrossRef] - Charfeddine, L.; Mrabet, Z. The impact of economic development and social-political factors on ecological footprint: A panel data analysis for 15 MENA countries. Renew. Sustain. Energy Rev.
**2017**, 76, 138–154. [Google Scholar] [CrossRef] - Alola, A.A.; Bekun, F.V.; Sarkodie, S.A. Dynamic impact of trade policy, economic growth, fertility rate, renewable and non-renewable energy consumption on ecological footprint in Europe. Sci. Total Environ.
**2019**, 685, 702–709. [Google Scholar] [CrossRef] - Shujah-ur-Rahman Chen, S.; Saud, S.; Saleem, N.; Bari, M.W. Nexus between financial development, energy consumption, income level, and ecological footprint in CEE countries: Do human capital and biocapacity matter? Environ. Sci. Pollut. Res.
**2019**. [Google Scholar] [CrossRef] - Olanipekun, I.O.; Olasehinde-Williams, G.O.; Alao, R.O. Agriculture and environmental degradation in Africa: The role of income. Sci. Total Environ.
**2019**, 692, 60–67. [Google Scholar] [CrossRef] - Adams, S.; Acheampong, A.O. Reducing carbon emissions: The role of renewable energy and democracy. J. Clean. Prod.
**2019**, 240, 118245. [Google Scholar] [CrossRef] - Bekun, F.V.; Alola, A.A.; Sarkodie, S.A. Toward a sustainable environment: Nexus between CO
_{2}emissions, resource rent, renewable and nonrenewable energy in 16-EU countries. Sci. Total Environ.**2019**, 657, 1023–1029. [Google Scholar] [CrossRef] [PubMed] - Paramati, S.R.; Apergis, N.; Ummalla, M. Dynamics of renewable energy consumption and economic activities across the agriculture, industry, and service sectors: Evidence in the perspective of sustainable development. Environ. Sci. Pollut. Res.
**2018**, 25, 1375–1387. [Google Scholar] [CrossRef] [PubMed] - Wang, Q.; Zhan, L. Assessing the sustainability of renewable energy: An empirical analysis of selected 18 European countries. Sci. Total Environ.
**2019**, 692, 529–545. [Google Scholar] [CrossRef] [PubMed] - Fourcroy, C.; Gallouj, F.; Decellas, F. Energy consumption in service industries: Challenging the myth of non-materiality. Ecol. Econ.
**2012**, 81, 155–164. [Google Scholar] [CrossRef] - Martínez, C.I.P.; Silveira, S. Analysis of energy use and CO
_{2}emission in service industries: Evidence from Sweden. Renew. Sustain. Energy Rev.**2012**, 16, 5285–5294. [Google Scholar] [CrossRef] - Sarkodie, S.A.; Adams, S. Renewable energy, nuclear energy, and environmental pollution: Accounting for political institutional quality in South Africa. Sci. Total Environ.
**2018**, 643, 1590–1601. [Google Scholar] [CrossRef] - Romm, J. The internet and the new energy economy. Resour. Conserv. Recycl.
**2002**, 36, 197–210. [Google Scholar] [CrossRef] - Jian, H.; Xu, M.; Zhou, L. Collaborative collection effort strategies based on the Internet + recycling business model. J. Clean. Prod.
**2019**, 241, 118120. [Google Scholar] [CrossRef] - Vita, G.; Lundström, J.R.; Hertwich, E.G.; Quist, J.; Ivanova, D.; Stadler, K.; Wood, R. The environmental impact of green consumption and sufficiency lifestyles scenarios in Europe: Connecting local sustainability visions to global consequences. Ecol. Econ.
**2019**, 164, 106322. [Google Scholar] [CrossRef] - Farjam, M.; Nikolaychuk, O.; Bravo, G. Experimental evidence of an environmental attitude-behavior gap in high-cost situations. Ecol. Econ.
**2019**, 166, 106434. [Google Scholar] [CrossRef][Green Version] - Feng, Y.; Wang, X.; Du, W.; Liu, J.; Li, Y. Spatiotemporal characteristics and driving forces of urban sprawl in China during 2003–2017. J. Clean. Prod.
**2019**, 241, 118061. [Google Scholar] [CrossRef] - Salahuddin, M.; Alam, K.; Ozturk, I. The effects of Internet usage and economic growth on CO
_{2}emissions in OECD countries: A panel investigation. Renew. Sustain. Energy Rev.**2016**, 62, 1226–1235. [Google Scholar] [CrossRef]

**Figure 1.**Temporal trends of global ecological footprint (EF) and biocapacity (BIO) (1961–2016). Data source: National Footprint Account results (2019 Edition) from the Global Footprint Network (GFN).

**Figure 2.**Evolution of “number of Earths” needed (1961–2016). Data source: National Footprint Account results (2019 Edition) from the GFN.

**Table 1.**Ecological footprint (EF) values and “number of Earths” of G20 countries for the time period 1990–2016.

1990 | 2000 | 2010 | 2016 | |||||
---|---|---|---|---|---|---|---|---|

EF | Earths | EF | Earths | EF | Earths | EF | Earths | |

Argentina | 3.07 | 1.49 | 3.13 | 1.69 | 3.25 | 1.91 | 3.37 | 2.06 |

Australia | 8.04 | 3.88 | 8.06 | 4.33 | 8.32 | 4.89 | 6.64 | 4.07 |

Brazil | 2.89 | 1.40 | 3.08 | 1.66 | 3.00 | 1.76 | 2.81 | 1.73 |

Canada | 8.94 | 4.32 | 9.10 | 4.90 | 8.34 | 4.90 | 7.74 | 4.75 |

China | 1.53 | 0.74 | 1.92 | 1.03 | 3.36 | 1.98 | 3.62 | 2.22 |

France | 5.59 | 2.70 | 5.54 | 2.98 | 5.25 | 3.09 | 4.45 | 2.73 |

Germany | 6.90 | 3.34 | 5.51 | 2.96 | 5.39 | 3.17 | 4.84 | 2.97 |

India | 0.78 | 0.38 | 0.86 | 0.46 | 1.07 | 0.63 | 1.17 | 0.72 |

Indonesia | 1.20 | 0.58 | 1.35 | 0.73 | 1.51 | 0.89 | 1.69 | 1.04 |

Italy | 5.18 | 2.51 | 5.60 | 3.01 | 5.29 | 3.11 | 4.44 | 2.72 |

Japan | 5.46 | 2.64 | 5.29 | 2.84 | 4.69 | 2.76 | 4.49 | 2.76 |

Republic of Korea | 3.74 | 1.81 | 5.06 | 2.72 | 5.88 | 3.46 | 6.00 | 3.68 |

Mexico | 2.50 | 1.21 | 2.85 | 1.53 | 3.18 | 1.87 | 2.60 | 1.60 |

Russia | 6.90 | 3.34 | 4.69 | 2.52 | 5.35 | 3.15 | 5.16 | 3.17 |

Saudi Arabia | 2.13 | 1.03 | 3.77 | 2.03 | 5.66 | 3.33 | 6.23 | 3.83 |

South Africa | 3.36 | 1.62 | 3.05 | 1.64 | 3.60 | 2.12 | 3.15 | 1.93 |

Turkey | 2.58 | 1.25 | 2.92 | 1.57 | 3.21 | 1.89 | 3.36 | 2.06 |

United Kingdom | 5.84 | 2.82 | 5.73 | 3.08 | 5.31 | 3.12 | 4.37 | 2.68 |

United States | 9.87 | 4.77 | 10.25 | 5.52 | 8.94 | 5.26 | 8.10 | 4.97 |

Abbreviation | Variable |
---|---|

EF | Ecological footprint |

URB | Urban population (% of total population) |

REN | Renewable energy consumption (% of total final energy consumption) |

SER | Services, value added (% of GDP) |

INT | Individuals using the internet (% of population) |

MYS | Mean years of schooling |

GNIPC | Gross national income per capita |

Developed Countries | ||
---|---|---|

Albania | France | North Macedonia |

Australia | Germany | Norway |

Austria | Greece | Poland |

Belarus | Hungary | Portugal |

Belgium | Ireland | Romania |

Bosnia and Herzegovina | Israel | Russian Federation |

Bulgaria | Italy | Serbia |

Canada | Japan | Slovak Republic |

Croatia | Republic of Korea | Slovenia |

Cyprus | Latvia | Spain |

Czech | Lithuania | Sweden |

Denmark | Moldova | Switzerland |

Estonia | Netherlands | United Kingdom |

Finland | New Zealand | United States |

Developing Countries | ||
---|---|---|

Angola | Ecuador | Niger |

Argentina | Egypt | Pakistan |

Bangladesh | Ethiopia | Peru |

Benin | Ghana | Philippines |

Bolivia | Guatemala | Rwanda |

Brazil | Guinea | Senegal |

Burkina Faso | Haiti | South Africa |

Burundi | India | Sri Lanka |

Cambodia | Indonesia | Thailand |

Cameroon | Kazakhstan | Tunisia |

Chad | Madagascar | Turkey |

Chile | Malawi | Uganda |

China | Malaysia | Venezuela |

Colombia | Mali | Vietnam |

Cote d’Ivoire | Mexico | Zambia |

Dominican Republic | Morocco | Zimbabwe |

**Table 5.**Statistical descriptions of the variables for all sample countries (1996–2015). Obs—observations; Min—minimum; Max—maximum.

Variable | Obs | Mean | SD | Min | Max |
---|---|---|---|---|---|

EF | 1790 | 3.40 | 2.22 | 0.50 | 10.48 |

URB | 1800 | 57.13 | 22.22 | 7.41 | 97.88 |

REN | 1796 | 33.77 | 29.10 | 0.61 | 98.09 |

SER | 1786 | 53.12 | 9.86 | 17.99 | 77.02 |

INT | 1778 | 27.40 | 28.85 | 0.00 | 96.81 |

MYS | 1773 | 8.12 | 3.40 | 0.90 | 14.10 |

GNIPC | 1794 | 14,562.42 | 13,688.90 | 450.00 | 68,100.00 |

Variable | Obs | Mean | SD | Min | Max |
---|---|---|---|---|---|

EF | 830 | 5.27 | 1.73 | 1.09 | 10.48 |

URB | 840 | 70.41 | 13.27 | 39.47 | 97.88 |

REN | 840 | 15.79 | 12.95 | 0.61 | 60.19 |

SER | 838 | 59.22 | 7.96 | 35.70 | 76.92 |

INT | 826 | 45.83 | 29.29 | 0.00 | 96.81 |

MYS | 832 | 10.93 | 1.49 | 6.50 | 14.10 |

GNIPC | 835 | 24,869.68 | 13,180.40 | 2180.00 | 68,100.00 |

Variable | Obs | Mean | SD | Min | Max |
---|---|---|---|---|---|

EF | 960 | 1.78 | 0.99 | 0.50 | 6.83 |

URB | 960 | 45.50 | 21.97 | 7.41 | 91.50 |

REN | 956 | 49.57 | 30.17 | 1.15 | 98.09 |

SER | 948 | 47.73 | 8.08 | 17.99 | 77.02 |

INT | 952 | 11.41 | 16.10 | 0.00 | 76.63 |

MYS | 941 | 5.63 | 2.58 | 0.90 | 11.70 |

GNIPC | 959 | 5587.91 | 5123.61 | 450.00 | 26,360.00 |

**Table 8.**Estimations of impacts of influencing factors and control variables for all sample countries.

Model (1) | Model (2) | Model (3) | Model (4) | Model (5) | Model (6) | |
---|---|---|---|---|---|---|

Coefficient (Prob.) | Coefficient (Prob.) | Coefficient (RSE) | Coefficient (RSE) | Coefficient (RSE) | Coefficient (RSE) | |

URB | −0.003 (0.53) | −0.033 *** (0.00) | −0.033 *** (0.01) | −0.029 ** (0.01) | −0.034 *** (0.01) | −0.030 ** (0.01) |

REN | −0.028 *** (0.00) | −0.031 *** (0.00) | −0.031 *** (0.01) | −0.026 *** (0.01) | −0.027 *** (0.01) | −0.025 *** (0.01) |

SER | −0.014 *** (0.00) | −0.018 *** (0.00) | −0.018 *** (0.00) | −0.018 *** (0.01) | −0.019 *** (0.01) | −0.019 *** (0.01) |

INT | −0.012 *** (0.00) | −0.005 *** (0.00) | −0.005 * (0.00) | −0.005 * (0.00) | −0.002 (0.00) | −0.004 * (0.00) |

MYS | 0.075 *** (0.00) | 0.013 (0.58) | 0.013 (0.05) | 0.015 (0.05) | 0.001 (0.05) | 0.010 (0.05) |

Ln(GNIPC) | −2.958 *** (0.00) | −0.956 *** (0.01) | −0.956 (0.80) | −0.078 (0.89) | 0.592 *** (0.18) | 0.591 *** (0.20) |

Ln(GNIPC)^{2} | 0.215 *** (0.00) | 0.093 *** (0.00) | 0.093 * (0.05) | 0.040 (0.06) | ||

Constant | 13.858 *** (0.00) | 8.228 *** (0.00) | 8.228 *** (3.16) | 4.353 (3.48) | 2.031 (1.29) | 1.673 (1.40) |

Prob > F-statistic | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |

Prob > chi^{2} | 0.00 | |||||

R-squared | 0.68 | 0.18 | 0.18 | 0.15 | 0.17 | 0.15 |

Obs | 1728 | 1728 | 1728 | 1729 | 1728 | 1729 |

Groups | 90 | 90 | 90 | 90 | 90 | 90 |

**Table 9.**Estimations of impacts of influencing factors and control variables for the developed countries.

Model (1) | Model (2) | Model (3) | Model (4) | |
---|---|---|---|---|

Coefficient (Prob.) | Coefficient (Prob.) | Coefficient (RSE) | Coefficient (RSE) | |

URB | 0.004 (0.64) | −0.046 *** (0.00) | −0.046 ** (0.02) | −0.046 ** (0.02) |

REN | −0.051 *** (0.00) | −0.058 *** (0.00) | −0.058 *** (0.01) | −0.046 *** (0.01) |

SER | −0.060 *** (0.00) | −0.077 *** (0.00) | −0.077 *** (0.01) | −0.069 *** (0.02) |

INT | −0.005 *** (0.01) | 0.003 (0.18) | 0.003 (0.00) | 0.003 (0.00) |

MYS | 0.122 *** (0.00) | 0.119 *** (0.00) | 0.119 (0.07) | 0.091 (0.08) |

Ln(GNIPC) | 4.498 *** (0.00) | 6.017 *** (0.00) | 6.017 *** (2.01) | 8.407 *** (2.32) |

Ln(GNIPC)^{2} | −0.188 *** (0.00) | −0.285 *** (0.00) | −0.285 ** (0.11) | −0.423 *** (0.13) |

Constant | −17.801 *** (0.00) | −18.954 *** (0.00) | −18.954 ** (8.83) | −29.425 *** (10.28) |

Prob > F-statistic | 0.00 | 0.00 | 0.00 | |

Prob > chi^{2} | 0.00 | |||

R-squared | 0.12 | 0.32 | 0.32 | 0.28 |

Obs | 810 | 810 | 810 | 811 |

Groups | 42 | 42 | 42 | 42 |

**Table 10.**Estimations of impacts of influencing factors and control variables for the developing countries.

Model (1) | Model (2) | Model (3) | Model (4) | |
---|---|---|---|---|

Coefficient (Prob.) | Coefficient (Prob.) | Coefficient (RSE) | Coefficient (RSE) | |

URB | −0.005 * (0.08) | −0.014 *** (0.00) | -0.014 (0.01) | −0.014 (0.01) |

REN | −0.008 *** (0.00) | −0.008 *** (0.00) | −0.008 ** (0.00) | −0.006 * (0.00) |

SER | −0.004 ** (0.02) | −0.004 ** (0.02) | −0.004 * (0.00) | −0.006 ** (0.00) |

INT | −0.005 *** (0.00) | −0.004 *** (0.00) | −0.004 * (0.00) | −0.006 * (0.00) |

MYS | −0.042 ** (0.02) | −0.061 *** (0.00) | −0.061 (0.06) | −0.042 (0.04) |

Ln(GNIPC) | −3.090 *** (0.00) | −2.759 *** (0.00) | −2.759 *** (0.91) | −2.941 *** (1.13) |

Ln(GNIPC)^{2} | 0.228 *** (0.00) | 0.210 *** (0.00) | 0.210 *** (0.07) | 0.225 *** (0.09) |

Constant | 12.724 *** (0.00) | 11.683 *** (0.00) | 11.683 *** (3.31) | 12.137 *** (4.01) |

Prob > F-statistic | 0.00 | 0.00 | 0.00 | |

Prob > chi^{2} | 0.00 | |||

R-squared | 0.63 | 0.39 | 0.39 | 0.39 |

Obs | 918 | 918 | 918 | 918 |

Groups | 48 | 48 | 48 | 48 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Zhang, S.; Zhu, D.; Zhang, J.; Li, L. Which Influencing Factors Could Reduce Ecological Consumption? Evidence from 90 Countries for the Time Period 1996–2015. *Appl. Sci.* **2020**, *10*, 678.
https://doi.org/10.3390/app10020678

**AMA Style**

Zhang S, Zhu D, Zhang J, Li L. Which Influencing Factors Could Reduce Ecological Consumption? Evidence from 90 Countries for the Time Period 1996–2015. *Applied Sciences*. 2020; 10(2):678.
https://doi.org/10.3390/app10020678

**Chicago/Turabian Style**

Zhang, Shuai, Dajian Zhu, Jiaping Zhang, and Lilian Li. 2020. "Which Influencing Factors Could Reduce Ecological Consumption? Evidence from 90 Countries for the Time Period 1996–2015" *Applied Sciences* 10, no. 2: 678.
https://doi.org/10.3390/app10020678