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Location matters for pro-environmental behavior: a spatial Markov Chains approach to proximity effects in differentiated waste collection

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Abstract

We analyze data on differentiated waste collection (as a proxy of pro-environmental behaviors) in Italian provinces in the years 1999–2012. We make use of a Markov Spatial Transition approach to model the dynamic of local transitions among different levels of environmental pro-sociality, and we find that behaviors, and in particular differentiated waste collecting habits, tend to be strongly influenced by proximity effects, so that provinces with good levels of environmental pro-sociality may positively influence nearby ones, and vice versa for provinces with poor levels of environmental pro-sociality. We also show that in the long term separate clusters with markedly different levels of differentiated waste collection rates emerge.

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Notes

  1. Our period of analysis consists of 14 years, so we have \(T = 13\) annual transitions.

  2. For reasons of space, we do not report the values of the unconditional transition matrix. Interested readers can request them to the authors.

  3. We describe more in detail the 5 states in Sect. 4.

  4. That is, estimates that do not take into account spatial dependence.

  5. In the Italian case, it is customary to distinguish between Southern regions, or Mezzogiorno (namely, Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, Sicilia and Sardegna), Northern regions (namely, Piemonte, Valle d’Aosta, Lombardia, Trentino Alto Adige, Veneto, Friuli Venezia Giulia, Liguria, Emilia Romagna), and Central regions (Toscana, Umbria, Marche and Lazio).

  6. The Moran scatter plot provides a tool for visual exploration of spatial autocorrelation (Anselin 1996, 2002a, b). The four different quadrants of the scatterplot identify four types of local spatial association between a province and its neighbors:

    • (HH) a province with a good pro-environmental behavior (high differentiated waste collection rate) surrounded by neighbors with good pro-environmental behavior (quadrant I);

    • (LH) a province with a poor pro-environmental behavior (low differentiated waste collection rate) surrounded by neighbors with good pro-environmental behavior (quadrant II);

    • (LL) a province with a poor pro-environmental behavior surrounded by neighbors with poor pro-environmental behavior (quadrant III);

    • (HL) a province with a good pro-environmental behavior surrounded by neighbors with poor pro-environmental behavior (quadrant IV).

    Quadrants I and III represent positive spatial dependence, while quadrants II and IV represent negative spatial dependence (Rey and Montouri 1999).

  7. The null hypothesis of the Moran’s I test is spatial independence. According to the results, we reject the null hypothesis at the 1 % level, and we conclude that the annual average of provinces’ differentiated waste collection rate presents spatial autocorrelation.

  8. The Moran’s I test, carried out on differentiated waste collection rates for each year analyzed, always rejects the null hypothesis of spatial independence. For reasons of space, we do not report these results, but interested readers can request them to the authors.

  9. The number of states (\(=\)5) is given by default by the software Space–Time Analysis of Regional Systems (STARS) and it is not editable.

  10. With n provinces, K states and t years, there are \({(t-1)\times K\times n}\) possible cases of transitions.

  11. The spatial lag is the average differentiated waste collection rate of neighboring provinces. The spatial lag is a weighted average, where the weights are represented by the elements of the contiguity matrix.

  12. “The ergodic distribution should be viewed as a “thought experiment” that illustrates how space may influence transition dynamics, rather than as a guide to what would transpire in reality” (Rey 2001). The ergodic distribution returned by the software is computed for each of the five transition matrices. For more details on the ergodic distribution concept, see Rey (2001) and Le Gallo (2004).

  13. Some conditional transition probabilities are computed on a small number of observations (in two cases there are only 1 and 2 observations, e.g., lines 11 and 15) and hence may be over- or underestimated. This problem occurs in all the empirical studies conducted with this method (Le Gallo 2004; Schettini et al. 2011).

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Authors and Affiliations

  1. Department of Economic and Legal Studies, University of Naples “Parthenope”, Via Ammiraglio Ferdinando Acton, 38, 80133, Naples, Italy

    Massimiliano Agovino

  2. Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, University “G. D’Annunzio” of Chieti-Pescara, Viale Pindaro, 42, 65127, Pescara, Italy

    Alessandro Crociata

  3. Department of Comparative Literature and Linguistics, IULM University, Via Carlo Bo, 1, 20143, Milan, Italy

    Pier Luigi Sacco

Authors
  1. Massimiliano Agovino
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  2. Alessandro Crociata
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  3. Pier Luigi Sacco
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Correspondence to Pier Luigi Sacco.

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Agovino, M., Crociata, A. & Sacco, P.L. Location matters for pro-environmental behavior: a spatial Markov Chains approach to proximity effects in differentiated waste collection. Ann Reg Sci 56, 295–315 (2016). https://doi.org/10.1007/s00168-015-0740-7

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