# The Impact of Consumption Patterns on the Generation of Municipal Solid Waste in China: Evidences from Provincial Data

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

- Are there any particular factors influencing MSW generation in China?
- How do these factors influence MSW generation in different regions of China?
- What is the future volume of MSW based on the future trend of these influencing factors?

## 2. Literature Review

#### 2.1. Factors Influencing MSW Generation

#### 2.2. Forecasting Methods for MSW

#### 2.3. Contributions of This Paper

## 3. Materials and Methods

#### 3.1. Data

#### 3.1.1. Dependent Variable: Municipal Solid Waste (MSW)

#### 3.1.2. Core Independent Variables

#### 3.1.3. Control Variables

#### 3.2. Research Framework and Models

#### 3.2.1. Step 1: Regression Analysis of Global Model

#### 3.2.2. Step 2: Clustering of Provinces: Regional Heterogeneity of MSW Generation

#### 3.2.3. Step 3: Regression Analysis of Local Models

#### 3.2.4. Step 4: Forecasting of MSW Generation

^{2}, which were computed as Equations (6) and (7). ${y}_{i}$ is the observed true value, ${f}_{i}$ is the forecasted value for province i, and n is the number of the observations [6]. MAPE can use the acquired data to reflect the degree of deviation from the predicted value to the observed true value. The smaller the MAPE is, the better the forecasting ability will be.

^{2}is the coefficient of determination, representing to what extent the variation in the dependent variable can be explained by the variation in the independent variable. The closer R

^{2}is to 1, the better the degree of fit of the regression equation is.

^{2}were used to test the forecasting ability.

## 4. Results

#### 4.1. Regression Results of Global Model

#### 4.2. Results of Clustering

#### 4.3. Regression Results of Local Models

#### 4.4. Forecasting Ability of MSW Generation

^{2}of Liaoning province are 10.98% and 0.88; in local model 2, the MAPE and R

^{2}of Hubei province are 10.68% and 0.93; and in local model 3, the MAPE and R

^{2}of Sichuan are 5.20% and 0.99. Therefore, we can safely conclude that these three local models have ideal forecasting ability by including influencing factors into models.

#### 4.5. Forecasting of MSW Generation in Five Years of Three Different Clusters

## 5. Analysis and Discussion

#### 5.1. MSW Generation in the Global Model

#### 5.2. MSW Generation in Local Models

#### 5.3. Future MSW Generation Forecasting in Liaoning, Hubei, and Sichuan Provinces of China

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

Variable | VIF | 1/VIF |
---|---|---|

PSE | 1.28 | 0.783 |

FC | 4.11 | 0.243 |

CC | 2.54 | 0.393 |

HC | 5.40 | 0.185 |

PGDP | 7.53 | 0.133 |

PD | 1.14 | 0.876 |

TIP | 2.63 | 0.380 |

AGE | 2.46 | 0.406 |

FS | 3.25 | 0.307 |

Mean VIF | 3.37 |

Variable | Local Model 1 | Local Model 2 | Local Model 3 |
---|---|---|---|

PSE | 1.89 | 1.65 | 1.68 |

FC | 6.32 | 1.73 | 2.27 |

CC | – | 1.86 | – |

HC | 6.66 | 2.19 | 1.44 |

PGDP | 4.33 | – | – |

PD | – | 1.91 | 1.21 |

TIP | 2.88 | 1.48 | 1.41 |

AGE | – | 1.53 | – |

FS | 3.65 | – | – |

Mean VIF | 4.29 | 1.76 | 1.60 |

## References

- Babayemi, J.O.; Dauda, K.T. Evaluation of solid waste generation, categories and disposal options in developing countries: A case study of Nigeria. J. Appl. Sci. Environ. Manag.
**2009**, 13, 83–88. [Google Scholar] [CrossRef] - Lu, J.W.; Zhang, S.; Hai, J.; Lei, M. Status and perspectives of municipal solid waste incineration in China: A comparison with developed regions. Waste Manag.
**2017**, 69, 170–186. [Google Scholar] [CrossRef] [PubMed] - Ogunjuyigbe, A.S.O.; Ayodele, T.R.; Alao, M.A. Electricity generation from municipal solid waste in some selected cities of Nigeria: An assessment of feasibility, potential and technologies. Renew. Sustain. Energy Rev.
**2017**, 80, 149–162. [Google Scholar] [CrossRef] - Bosire, E.; Oindo, B.; Atieno, J.V. Modeling Household Solid Waste Generation in Urban Estates Using SocioEconomic and Demographic Data, Kisumu City, Kenya. Available online: https://repository.maseno.ac.ke/handle/123456789/441 (accessed on 28 April 2019).
- Han, Z.; Liu, Y.; Zhong, M.; Shi, G.; Li, Q.; Zeng, D.; Zhang, Y.; Fei, Y.; Xie, Y. Influencing factors of domestic waste characteristics in rural areas of developing countries. Waste Manag.
**2018**, 72, 45–54. [Google Scholar] [CrossRef] [PubMed] - Oribe-Garcia, I.; Kamara-Esteban, O.; Martin, C.; Macarulla-Arenaza, A.M.; Alonso-Vicario, A. Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay. Waste Manag.
**2015**, 9, 26–34. [Google Scholar] [CrossRef] - Ghinea, C.; Drăgoi, E.N.; Comăniţă, E.-D.; Gavrilescu, M.; Câmpean, T.; Curteanu, S.; Gavrilescu, M. Forecasting municipal solid waste generation using prognostic tools and regression analysis. J. Environ. Manag.
**2016**, 182, 80–93. [Google Scholar] [CrossRef] [PubMed] - Fu, H.Z.; Li, Z.S.; Wang, R.H. Estimating municipal solid waste generation by different activities and various resident groups in five provinces of China. Waste Manag.
**2015**, 41, 3–11. [Google Scholar] [CrossRef] - Chen, C. Spatial inequality in municipal solid waste disposal across regions in developing countries. Int. J. Environ. Sci. Technol.
**2010**, 7, 447–456. [Google Scholar] [CrossRef][Green Version] - Gu, B.; Wang, H.; Chen, Z.; Jiang, S.; Zhu, W.; Liu, M.; Chen, Y.; Wu, Y.; He, S.; Cheng, R. Characterization, quantification and management of household solid waste: A case study in China. Resour. Conserv. Recycl.
**2015**, 98, 67–75. [Google Scholar] [CrossRef] - Wang, Z.; Geng, L. Carbon emissions calculation from municipal solid waste and the influencing factors analysis in China. J. Clean. Prod.
**2015**, 104, 177–184. [Google Scholar] [CrossRef] - Hage, O.; Sandberg, K.; Söderholm, P.; Berglund, C. The regional heterogeneity of household recycling: A spatial-econometric analysis of Swedish plastic packing waste. Lett. Spat. Resour. Sci.
**2018**, 11, 245–267. [Google Scholar] [CrossRef] - Thanh, N.P.; Matsui, Y.; Fujiwara, T. Household solid waste generation and characteristic in a Mekong Delta city, Vietnam. J. Environ. Manag.
**2010**, 91, 2307–2321. [Google Scholar] [CrossRef] [PubMed] - Qu, X.Y.; Li, Z.S.; Xie, X.Y.; Sui, Y.M.; Yang, L.; Chen, Y. Survey of composition and generation rate of household wastes in Beijing, China. Waste Manag.
**2009**, 29, 2618–2624. [Google Scholar] [CrossRef] [PubMed] - Grazhdani, D. Assessing the variables affecting on the rate of solid waste generation and recycling: An empirical analysis in Prespa Park. Waste Manag.
**2016**, 48, 3–13. [Google Scholar] [CrossRef] - Trang, P.T.T.; Dong, H.Q.; Toan, D.Q.; Hanh, N.T.X.; Thu, N.T. The effects of socio-economic factors on household solid waste generation and composition: A case study in Thu Dau Mot, Vietnam. Energy Procedia.
**2017**, 107, 253–258. [Google Scholar] [CrossRef] - Ramachandra, T.; Bharath, H.; Kulkarni, G.; Han, S.S. Municipal solid waste: Generation, composition and GHG emissions in Bangalore, India. Renew. Sustain. Energy Rev.
**2018**, 82, 1122–1136. [Google Scholar] [CrossRef] - Prades, M.; Gallardo, A.; Ibàñez, M.V. Factors determining waste generation in Spanish towns and cities. Environ. Monit. Assess.
**2015**, 187, 4098. [Google Scholar] [CrossRef] - Mahees, M.T.M.; Sivayoganathan, C.; Basnayake, B.F.A. Consumption, Solid Waste Generation and Water Pollution in Pinga Oya Catchment Area. Trop. Agric. Res.
**2011**, 22, 239–250. [Google Scholar] [CrossRef] - Chhay, L.; Reyad, M.A.H.; Suy, R.; Islam, M.R.; Mian, M.M. Municipal solid waste generation in China: Influencing factor analysis and multi-model forecasting. J. Mater. Cycles Waste Manag.
**2018**, 20, 1761–1770. [Google Scholar] [CrossRef] - Getahun, T.; Mengistie, E.; Haddis, A.; Wasie, F.; Alemayehu, E.; Dadi, D.; Van Gerven, T.; Van der Bruggen, B. Municipal solid waste generation in growing urban areas in Africa: Current practices and relation to socioeconomic factors in Jimma, Ethiopia. Environ. Monit. Assess.
**2012**, 184, 6337–6345. [Google Scholar] [CrossRef] - Xu, L.; Lin, T.; Xu, Y.; Xiao, L.; Ye, Z.; Cui, S. Path analysis of factors influencing household solid waste generation: A case study of Xiamen Island, China. J. Mater. Cycles Waste Manag.
**2016**, 18, 377–384. [Google Scholar] [CrossRef] - Khan, D.; Kumar, A.; Samadder, S. Impact of socioeconomic status on municipal solid waste generation rate. Waste Manag.
**2016**, 49, 15–25. [Google Scholar] [CrossRef] [PubMed] - Keser, S.; Duzgun, S.; Aksoy, A. Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey. Waste Manag.
**2012**, 32, 359–371. [Google Scholar] [CrossRef] [PubMed] - Hockett, D.; Lober, D.J.; Pilgrim, K. Determinants of per capita municipal solid waste generation in the Southeastern United States. J. Environ. Manag.
**1995**, 45, 205–218. [Google Scholar] [CrossRef] - Kolekar, K.A.; Hazra, T.; Chakrabarty, S.N. A review on prediction of municipal solid waste generation models. Procedia Environ. Sci.
**2016**, 35, 238–244. [Google Scholar] [CrossRef] - Abbasi, M.; El Hanandeh, A. Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag.
**2016**, 56, 13–22. [Google Scholar] [CrossRef] - Beigl, P.; Lebersorger, S.; Salhofer, S. Modelling municipal solid waste generation: A review. Waste Manag.
**2008**, 28, 200–214. [Google Scholar] [CrossRef] - Beigl, P.; Wassermann, G.; Schneider, F.; Salhofer, S. Forecasting Municipal Solid Waste Generation in Major European Cities. In Proceedings of the 2nd International Congress on Environmental Modelling and Software, Osnabruck, Germany, 14–17 June 2004; University of Osnabruck: Osnabruck, Germany, 2004. [Google Scholar]
- Xu, L.; Gao, P.; Cui, S.; Liu, C. A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China. Waste Manag.
**2013**, 33, 1324–1331. [Google Scholar] [CrossRef] - Navarro-Esbrı, J.; Diamadopoulos, E.; Ginestar, D. Time series analysis and forecasting techniques for municipal solid waste management. Resour. Conserv. Recycl.
**2002**, 35, 201–214. [Google Scholar] [CrossRef] - Intharathirat, R.; Salam, P.A.; Kumar, S.; Untong, A. Forecasting of municipal solid waste quantity in a developing country using multivariate grey models. Waste Manag.
**2015**, 39, 3–14. [Google Scholar] [CrossRef] - Vučijak, B.; Kurtagić, S.M.; Silajdžić, I. Multicriteria decision making in selecting best solid waste management scenario: A municipal case study from Bosnia and Herzegovina. J. Clean. Prod.
**2016**, 130, 166–174. [Google Scholar] [CrossRef] - Vieira, V.H.A.D.M.; Matheus, D.R. The impact of socioeconomic factors on municipal solid waste generation in São Paulo, Brazil. Waste Manag. Res.
**2018**, 36, 79–85. [Google Scholar] [CrossRef] [PubMed] - Otoniel, B.D.; Liliana, M.B.; Francelia, P.G. Consumption patterns and household hazardous solid waste generation in an urban settlement in México. Waste Manag.
**2008**, 28, S2–S6. [Google Scholar] [CrossRef] [PubMed] - Johnstone, N.; Labonne, J. Generation of household solid waste in OECD countries: An empirical analysis using macroeconomic data. Land Econ.
**2004**, 80, 529–538. [Google Scholar] [CrossRef] - Altland, H.W. Regression analysis: Statistical modeling of a response variable. Technometrics
**1999**, 41, 367–368. [Google Scholar] [CrossRef] - Hausman, J.; Stock, J.H.; Yogo, M. Asymptotic properties of the Hahn–Hausman test for weak-instruments. Econ. Lett.
**2005**, 89, 333–342. [Google Scholar] [CrossRef][Green Version] - Fan, J.L.; Zhang, Y.J.; Wang, B. The impact of urbanization on residential energy consumption in China: An aggregated and disaggregated analysis. Renew. Sustain. Energy Rev.
**2017**, 75, 220–233. [Google Scholar] [CrossRef] - Binder, C.R.; Mosler, H.J. Waste-resource flows of short-lived goods in households of Santiago de Cuba. Resour. Conserv. Recycl.
**2007**, 51, 265–283. [Google Scholar] [CrossRef][Green Version] - Ahmad, A.; Dey, L. A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl. Eng.
**2007**, 63, 503–527. [Google Scholar] [CrossRef] - Driscoll, J.C.; Kraay, A.C. Consistent covariance matrix estimation with spatially dependent panel data. Rev. Econ. Stat.
**1998**, 80, 549–560. [Google Scholar] [CrossRef] - Lau, K.M.; Li, S.M. Commercial housing affordability in Beijing, 1992–2002. Habitat Int.
**2006**, 30, 614–627. [Google Scholar] [CrossRef]

**Figure 1.**Comparison of municipal solid waste volume in China and Organization for Economic Co-operation and Development countries (Source: OECD statistics, https://stats.oecd.org/).

**Figure 2.**The flowchart for the methodology used in model building and forecasting of MSW generation.

Independent Variables | Data Collection | Methods Used | References | ||
---|---|---|---|---|---|

Level ^{a} | Type ^{b} | Models ^{c} | Methods ^{d} | ||

GDP | C | P | MLR and L | RA | Lu et al. [2] |

P | P | MLR | GMM | Wang and Geng [11] | |

C | TM | F | LR and ANN | Chhay et al. [20] | |

Income | CT | S | MLR | ANOVA and RCA | Gu et al. [10] |

UR | CS | MLR | RS and Q | Bosire et al. [4] | |

CT | P | MLR | EKC | Chen [9] | |

CT | S | MLR | Q and OLS | Trang et al. [16] | |

Family size | CT | S | MLR | ANOVA and RCA | Gu et al. [10] |

CT | S | — | RS | Getahun et al. [21] | |

UR | S | PA | CA and PA | Xu et al. [22] | |

Education | UR | S | PA | CA and PA | Xu et al. [22] |

CT | S | — | ST and RS and CA | Khan et al. [23] | |

Consumption expenditure | UR | CS | MLR | RS and Q | Bosire et al. [4] |

P | S | LR | CA | Han et al. [5] | |

Population density | CT | CS | SEM and SL | SE | Hage et al. [12] |

CT | P | MLR | CA and OLS | Oribe-Garcia et al. [6] | |

P | SP | MLR and GWR | OLS and SAR | Keser et al. [24] | |

Retail sales | S | P | MLR | CA and ST | Hockett et al. [25] |

Unemployment rate | CT | CS | SEM and SL | SE | Hage et al. [12] |

CT | S | MLR | CA and RA | Prades et al. [18] | |

Urbanization rate | CT | S | MLR | ANOVA and RCA | Thanh et al. [13] |

Industrial structure | P | P | MLR | CA and GMM | Wang and Geng [11] |

^{a}Data level: CT—city level; UR—Urban residential level; C—country level; S—state level; P—province level;

^{b}Data type: S—survey data; CS—cross-sectional data; P—panel data; SP—spatial data; TM—time-series data;

^{c}Models: LR—linear regression model; MLR—multiple linear regression model; PA—path analysis model; SEM—spatial error model; SL—spatial lag model; L—logistics model; F—fuzzy model; GWR—Geographically-Weighted Regression model.

^{d}Methods: ANN- Artificial Neural Network; OLS- Ordinary Least Square; ANOVA- Analysis of Variance; RCA—rank correlation analysis; RS—random sampling; Q—questionnaires; CA—correlation analysis; PA—path analysis; SE—spatial econometric; EKC—environmental Kuznets curve; RA—regression analysis; ST—Score tests; GMM—generalized moment method; SAR—spatial-auto regression; GWR—geographically-weighted regression.

Methods | Models ^{b} | Period Forecasted | Factors Involved ^{a} | Reference | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

P | GDP | CE | U | PD | FS | E | A | I | ||||

Regression | MLR | 2013–2023 | √ | √ | Ghinea et al. [7] | |||||||

MLR | 2016–2030 | √ | √ | √ | Chhay et al. [19] | |||||||

MLR | 10 years | √ | √ | √ | Beigl et al. [29] | |||||||

Time-series | L and EGC | 2013–2023 | Ghinea et al. [7] | |||||||||

SARIMA | Monthly and daily data | Navarro-Esbrı et al. [31] | ||||||||||

SARIMA | Month-scale | Xu et al. [30] | ||||||||||

GM(1,1) | 2016–2030 | √ | √ | √ | Chhay et al. [19] | |||||||

Grey models | GM(1,n) | 2013–2030 | √ | √ | √ | √ | √ | Intharathirat et al. [32] | ||||

GM(1,1) | 2010–2020 | Xu et al. [30] | ||||||||||

Scenario analysis | — | 2016–2030 | √ | √ | √ | Chhay et al. [19] | ||||||

— | √ | Vučijak et al. [33] | ||||||||||

Artificial neural network | ANFIS and SVM | Long term | Abbasi and El Hanandeh [27] |

^{a}Factors involved: P—population; CE—consumption expenditure; U—urbanization; PD—population density; FS—family size; EM—employment; A-age; I—income;

^{b}Models: MLR—multiple linear regression models; L—linear model; Q—quadratic model; EGC—exponential growth curve; SARIMA—seasonal autoregressive moving average; GM(1,1)-Grey Model(1,1); GM(1,n)-Grey Model(1,n); ANFIS-Adaptive Network-based Fuzzy Inference System; SVM- Support Vector Machine.

Variables | Abbreviation | Unit | Mean | Standard Deviation | Min | Max |
---|---|---|---|---|---|---|

Municipal solid waste generation | MSW | 10 thousand tons | 566.7 | 411.5 | 63.6 | 2391 |

Financial expenditure for general public services | PSE | Billion yuan | 295.9 | 174.2 | 36.4 | 979.7 |

Household expenditure on food | FC | yuan | 4020.1 | 986 | 2600.4 | 7989.8 |

Household expenditure on clothing | CC | yuan | 1177.5 | 257 | 452.9 | 1893.7 |

Household expenditure on housing | HC | yuan | 1086.8 | 260.3 | 641.9 | 1841.9 |

Per capita GDP | PGDP | yuan per person | 35,168.6 | 19,526 | 7940.8 | 103,588.6 |

Population density | PD | Square kilometers per person | 2777.8 | 1226 | 622 | 5967 |

Tertiary industry proportion | TIP | % | 42 | 9 | 28.6 | 80.2 |

Age structure | AGE | % | 35.5 | 6.5 | 19.3 | 55.1 |

Family size | FS | people per household | 3.1 | 0.3 | 2.3 | 3.9 |

MSW | PSE | FC | CC | HC | PGDP | PD | TIP | AGE | FS | |
---|---|---|---|---|---|---|---|---|---|---|

MSW | 1.00 | |||||||||

PSE | 0.854 *** | 1.00 | ||||||||

FC | 0.426 *** | 0.267 *** | 1.00 | |||||||

CC | 0.171 ** | 0.141 * | 0.316 *** | 1.00 | ||||||

HC | 0.475 *** | 0.256 *** | 0.765 *** | 0.425 *** | 1.00 | |||||

PGDP | 0.390 *** | 0.259 *** | 0.763 *** | 0.617 *** | 0.868 *** | 1.00 | ||||

PD | −0.071 | 0.0112 | −0.162 ** | −0.132 * | −0.23 *** | −0.188 ** | 1.000 | |||

TIP | 0.176 ** | 0.011 | 0.703 ** | 0.431 *** | 0.579 *** | 0.677 *** | −0.187 ** | 1.00 | ||

AGE | −0.292 *** | 0.023 | −0.368 *** | −0.490 *** | −0.602 *** | −0.625 *** | 0.091 | −0.398 *** | 1.00 | |

FS | −0.300 *** | −0.142 * | −0.510 *** | −0.687 *** | −0.606 *** | −0.694 *** | 0.244 *** | −0.433 *** | 0.668 *** | 1.00 |

Global Model | Local Model 1 | Local Model 2 | Local Model 3 | |
---|---|---|---|---|

Hausman test | χ^{2}(7) = 110.33 *** | χ^{2}(5) = 22.03 *** | χ^{2}(6) = 27.30 ** | χ^{2}(4) = 30.43 ** |

Heteroscedasticity | χ^{2}(30) = 2388.61 *** | χ^{2}(10) = 661.93 *** | χ^{2}(12) = 4711.72 *** | χ^{2}(8) = 63.34 *** |

Serial correlation | F(1, 29) = 53.66 *** | F(1, 9) = 29.907 *** | F(1, 11) = 46.750 *** | F(1, 7) = 46.324 *** |

Cross-sectional dependence | 3.234 *** | 1.201 | 4.123 *** | 1.593 |

Robust F Statistics | F(9, 29) = 35,847.45 *** | F(6, 9) = 1230.12 *** | F(7, 11) = 246.31 *** | F(5, 7) = 113.16 *** |

Independent Variables | Global Model | Local Model 1 | Local Model 2 | Local Model 3 |
---|---|---|---|---|

PSE | 0.738 ** (3.87) | 1.421 ** (4.71) | 0.473 * (2.65) | 0.223 (1.55) |

FC | 0.194 ** (2.98) | 0.444 *** (3.42) | 0.526 *** (7.76) | 0.604 *** (7.16) |

CC | −0.564 *** (−15.03) | – | −2.393 *** (−7.23) | – |

HC | −0.620 *** (−3.79) | −2.364 *** (−5.84) | 1.131 * (3.01) | −0.434 *** (−2.44) |

PGDP | 0.008 *** (6.06) | 0.010 *** (9.59) | – | – |

PD | −0.027 * (−2.51) | – | −0.051 ** (−4.24) | 0.013 (1.91) |

TIP | −2.254 (−1.55) | −7.353 * (−2.90) | 0.320 (0.23) | 2.193 * (2.94) |

AGE | −3.066 * (−2.56) | – | −5.526 *** (−3.17) | – |

FS | 123.279 *** (5.14) | 212.72 ** (4.53) | – | – |

Constant | 519.923 *** (5.64) | 586.004 * (2.68) | 653.157 *** (6.21) | −161.416 (−1.61) |

R^{2} | 0.5380 | 0.7040 | 0.3443 | 0.7642 |

Liaoning | Hubei | Sichuan | ||||
---|---|---|---|---|---|---|

Real MSW | Predicted MSW | Real MSW | Predicted MSW | Real MSW | Predicted MSW | |

2007 | 771.4 | 686.5 | 673.2 | 719.4 | 548.5 | 599.4 |

2008 | 796.7 | 701.2 | 680.8 | 751.9 | 551 | 627.7 |

2009 | 813.3 | 746.3 | 680.6 | 771.4 | 590.1 | 644.1 |

2010 | 837.3 | 763.2 | 711.1 | 778.1 | 656 | 659.7 |

2011 | 876 | 791.8 | 736.3 | 819.0 | 669 | 698.4 |

2012 | 929.9 | 856.1 | 716.6 | 850.2 | 702.8 | 732.7 |

2013 | 927.1 | 889.1 | 745.8 | 855.3 | 750.7 | 764.1 |

2014 | 917.1 | 782.6 | 739.3 | 862.8 | 780 | 779.1 |

2015 | 933.2 | 701.9 | 832.2 | 868.9 | 823.6 | 796.4 |

2016 | 933.05 | 852.5 | 880.1 | 970.9 | 886.7 | 839.9 |

MAPE | 0.1098 | 0.1068 | 0.0520 | |||

R^{2} | 0.88 | 0.93 | 0.99 |

^{a}MAPE—Mean Absolute Percentage Error. (Units: ten thousand tons).

© 2019 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**

Liu, J.; Li, Q.; Gu, W.; Wang, C. The Impact of Consumption Patterns on the Generation of Municipal Solid Waste in China: Evidences from Provincial Data. *Int. J. Environ. Res. Public Health* **2019**, *16*, 1717.
https://doi.org/10.3390/ijerph16101717

**AMA Style**

Liu J, Li Q, Gu W, Wang C. The Impact of Consumption Patterns on the Generation of Municipal Solid Waste in China: Evidences from Provincial Data. *International Journal of Environmental Research and Public Health*. 2019; 16(10):1717.
https://doi.org/10.3390/ijerph16101717

**Chicago/Turabian Style**

Liu, Jinhui, Qing Li, Wei Gu, and Chen Wang. 2019. "The Impact of Consumption Patterns on the Generation of Municipal Solid Waste in China: Evidences from Provincial Data" *International Journal of Environmental Research and Public Health* 16, no. 10: 1717.
https://doi.org/10.3390/ijerph16101717