| Literature DB >> 20619628 |
Yung Yau1.
Abstract
Efficacy of waste recycling is one of the key determinants of environmental sustainability of a city. Like other pro-environmental activities, waste recycling cannot be successfully accomplished by just one or two people, but only by a concerted effort of the community. The collective-action dilemma creates a common underlying difficulty in formulating workable solutions to many environmental problems. With a view to the non-excludability of the outcome, rationality drives people to free-ride efforts of others in waste recycling. To solve this free-rider problem, some scholars suggest the use of economic incentive. This article attempts to study the impacts of reward schemes on waste recycling behaviour of residents in 122 private housing estates in Hong Kong. The study is differentiable from the others as the latter mainly focus on domestic waste recycling in low-rise low-density housing while this one looks into the same in a high-rise high-density residential setting. According to the results of analyses on a set of aggregate data, reward schemes are found to have a significant positive relationship with the per-household weight of recyclables collected, keeping other things constant. The research findings suggest that economic incentives do work in promoting waste recycling in Hong Kong. Practical and policy implications follow.Entities:
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Year: 2010 PMID: 20619628 PMCID: PMC7127748 DOI: 10.1016/j.wasman.2010.06.009
Source DB: PubMed Journal: Waste Manag ISSN: 0956-053X Impact factor: 7.145
Comparative figures of total amount of municipal waste generated per gross domestic product in developed jurisdictions (Environmental Protection Department, 2009, Eurostat, 2010, The World Bank Group, 2010; author’s calculation).
| Country or City | Municipal waste per gross domestic product (g/US$) | |
|---|---|---|
| Year 2007 | Year 2008 | |
| Hong Kong | 16.62 | 15.97 |
| Germany | 14.43 | 13.06 |
| France | 12.96 | 11.81 |
| Finland | 10.96 | 10.22 |
| Sweden | 10.43 | 9.89 |
| United Kingdom | 12.59 | 13.11 |
| Norway | 5.99 | 5.19 |
| Switzerland | 12.88 | 11.58 |
| Czech Republic | 17.47 | 14.74 |
| Spain | 18.43 | 16.33 |
| Austria | 13.32 | 12.04 |
Fig. 1Payoff matrix of a prisoner’s dilemma.
Fig. 2Payoff matrix of a recycling game.
Fig. 3Payoff matrix of a recycling game with economic incentives.
Figures on waste disposal in Hong Kong (Environmental Protection Department, 2006, Environmental Protection Department, 2009).
| Year | Average daily quantity of domestic waste | Per-capita domestic waste disposal rate | Recycling rate for domestic waste |
|---|---|---|---|
| 2001 | 7551 | 1.12 | 10 |
| 2002 | 7519 | 1.11 | 13 |
| 2003 | 7402 | 1.10 | 14 |
| 2004 | 7014 | 1.03 | 14 |
| 2005 | 6828 | 1.00 | 16 |
| 2006 | 6634 | 0.97 | 20 |
| 2007 | 6372 | 0.92 | 23 |
| 2008 | 6081 | 0.87 | Not yet available |
Descriptive statistics of the continuous variables.
| Variable | Measurement unit | Maximum | Mean | Minimum | Standard deviation |
|---|---|---|---|---|---|
| kg per household | 149.94 | 66.22 | 15.19 | 28.16 | |
| % | 73.70 | 37.83 | 10.20 | 14.81 | |
| % | 54.75 | 46.07 | 36.91 | 3.27 | |
| years | 51.00 | 38.16 | 32.00 | 3.04 | |
| HK$ | 94650.00 | 35367.05 | 14000.00 | 19228.29 | |
| Nos. | 4.30 | 3.00 | 2.10 | 0.43 | |
| % | 98.30 | 79.22 | 36.50 | 13.49 | |
| Nos. | 6.12 | 3.88 | 2.31 | 0.66 |
Results of the Ordinary least square estimation of Eq. (2) in linear form.
| Variable | Coefficient | Standard error | ||
|---|---|---|---|---|
| Constant | −50.54384 | 57.04826 | −0.885984 | 0.3775 |
| 6.133905 | 29.71509 | 0.206424 | 0.8368 | |
| −38.37743 | 79.74880 | −0.481229 | 0.6313 | |
| 1.756570 | 0.714964 | 2.456864 | 0.0155 | |
| 0.000338 | 0.000176 | 1.924915 | 0.0568 | |
| 5.199810 | 8.558664 | 0.607549 | 0.5447 | |
| 0.958595 | 16.61010 | 0.057712 | 0.9541 | |
| 5.794764 | 4.827127 | 1.200458 | 0.2325 | |
| 2.003168 | 5.061237 | 0.395786 | 0.6930 | |
| 19.53496 | 5.121183 | 3.814541 | 0.0002 | |
| 0.411224 | Akaike info criterion | 9.139872 | ||
| Adjusted | 0.363912 | Schwarz criterion | 9.369710 | |
| Log likelihood | −547.5322 | Durbin–Watson statistic | 1.903642 | |
| 8.691692 | No. of included observations | 122 | ||
| Prob ( | 0.000000 | Dependent variable | ||
Notes: All coefficients were estimated with White’s Heteroskedasticity-consistent standard errors and covariance.
denotes the estimated coefficient of the variable to be significant at the 10% level.
denotes the estimated coefficient of the variable to be significant at the 5% level.
denotes the estimated coefficient of the variable to be significant at the 1% level.
Results of the Ordinary least square estimation of Eq. (2) in quadratic form.
| Variable | Coefficient | Standard error | ||
|---|---|---|---|---|
| Constant | −229.5649 | 382.7060 | −0.599847 | 0.5499 |
| −54.06081 | 100.0339 | −0.540425 | 0.5900 | |
| −7.87627 | 115.6493 | −0.068105 | 0.9458 | |
| 610.6751 | 1554.119 | 0.392940 | 0.6952 | |
| −737.8839 | 1661.785 | −0.444031 | 0.6579 | |
| 5.614668 | 8.21447 | 0.683509 | 0.4958 | |
| −0.056971 | 0.102802 | −0.554177 | 0.5806 | |
| 0.001283 | 0.000569 | 2.255191 | 0.0262 | |
| −4.21 × 10−9 | 2.93 × 10−9 | −1.439055 | 0.1531 | |
| −33.90555 | 56.23529 | −0.602923 | 0.5479 | |
| 5.270884 | 9.206628 | 0.572510 | 0.5682 | |
| −101.5055 | 129.4657 | −0.784034 | 0.4348 | |
| 71.77415 | 93.93196 | 0.764108 | 0.4465 | |
| 39.72285 | 30.52495 | 1.301324 | 0.1960 | |
| −4.267796 | 3.755167 | −1.136513 | 0.2583 | |
| 0.937532 | 5.703734 | 0.164372 | 0.8698 | |
| 21.26439 | 5.348187 | 3.976001 | 0.0001 | |
| 0.439450 | Akaike info criterion | 9.205500 | ||
| Adjusted | 0.354033 | Schwarz criterion | 9.596224 | |
| Log likelihood | −544.5355 | Durbin–Watson statistic | 1.954437 | |
| 5.144751 | No. of included observations | 122 | ||
| Prob ( | 0.000000 | Dependent variable | ||
Notes: All coefficients were estimated with White’s Heteroskedasticity-consistent standard errors and covariance
denotes the estimated coefficient of the variable to be significant at the 10% level.
denotes the estimated coefficient of the variable to be significant at the 5% level.
Results of the Ordinary least square estimation of Eq. (2) in log-linear form.
| Variable | Coefficient | Standard error | ||
|---|---|---|---|---|
| Constant | −572.0658 | 153.0904 | −3.736784 | 0.0003 |
| Ln( | −12.92357 | 11.4687 | −1.126856 | 0.2622 |
| Ln( | −15.37678 | 36.55991 | −0.420591 | 0.6749 |
| Ln( | 56.67374 | 26.82786 | 2.112495 | 0.0369 |
| Ln( | 33.72388 | 12.03258 | 2.802713 | 0.0060 |
| Ln( | 3.293777 | 23.5577 | 0.139817 | 0.8891 |
| Ln( | −0.514232 | 10.3177 | −0.04984 | 0.9603 |
| Ln( | 23.03255 | 17.79533 | 1.294303 | 0.1982 |
| 1.539936 | 4.971181 | 0.309773 | 0.7573 | |
| 20.34532 | 5.109444 | 3.981904 | 0.0001 | |
| 0.428796 | Akaike info criterion | 9.109573 | ||
| Adjusted | 0.392896 | Schwarz criterion | 9.339411 | |
| Log likelihood | −545.6840 | Durbin–Watson statistic | 1.942284 | |
| 9.341896 | No. of included observations | 122 | ||
| Prob ( | 0.000000 | Dependent variable | ||
Notes: All coefficients were estimated with White’s Heteroskedasticity-consistent standard errors and covariance.
denotes the estimated coefficient of the variable to be significant at the 5% level.
denotes the estimated coefficient of the variable to be significant at the 1% level.