https://orcid.org/0000-0001-9003-8106
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ABSTRACT
We test the effectiveness of environmental and energy policies, complying with legal requirements and followed voluntarily, on endeavours towards green and energy transitions. With this aim, a sample of Italian listed firms in the period 2008–19 is investigated. The empirical framework is developed by means of a propensity score matching analysis with multiple treatments, implemented in both a panel data and cross-sectional contexts. Our results show that regional interventions operate as a push/pull mechanism in driving companies to be more environmentally friendly. This is important for identification of policy-driven changes which make some regions more successful than others in sustainability transition.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
1. The years 2005–07 are not included because of the very low number of items.
2. Specifically, Lombardy exhibits the highest concentration of firms (39.68%), followed by Emilia Romagna, Lazio, Piedmont and Veneto (15.87%, 14.29%, 7.94% and 6.35%, respectively). The remaining companies (15.87%) are in Tuscany, Liguria, Friuli Venezia Giulia, Sardinia and Marche.
3. For further information, see https://www.refinitiv.com/content/dam/marketing/en_us/documents/quick-reference-guides/esg-data-in-eikon-march-2021.pdf/.
4. Data on employment and/or unemployment rates by NUTS-3 regions have been available only since 2018 (data source: ISTAT).
5. European Directive 2014/95/EU, the ‘Non-Financial Reporting Directive’, has been in force in Italy since 25 January 2017. It requires public-interest companies, including banks, insurance companies and listed firms with more than 500 employees, to disclose information on how they manage social and environmental challenges. Italian law specifies that the non-financial statement must report at least the following information in the environmental section: greenhouse gas emissions and polluting emissions into the atmosphere, the use of energy and water resources, and the impact on the environment and on health and safety. Risk factors and other significant environmental and health risk factors must also be reported.
6. The panel data framework is characterized by 63 firms in the period 2008–19.
7. These multinomial logistic regressions were also computed by using per capita GDP and unemployment rate by NUTS-2 regions. Results are mainly in line with those reported in . They are available from the authors upon request.
8. The number of neighbours used to calculate the matched outcome is five. Various calliper widths of standard deviations (i.e., between 0.20 and 0.70 with increment 0.05) were tested. When possible, a calliper width equal to 0.20 is preferred, as indicated by Wang et al. (Citation2013) and Santika et al. (Citation2017).
9. The following diagnostic tests, computed before and after matching, were run: (1) the pseudo-R2 associated with the estimations of the propensity scores; (2) the likelihood-ratio test of the joint insignificance of all the regressors (LR χ2) and the corresponding p-values (p > χ2); (3) the values of the mean and median bias and the associated reduction in bias (in percentage points); and (4) the Rubin B and R tests (Rubin, Citation2001).
10. If spatial effects are present in the treatment assignment, they violate the stable unit treatment value assumption for empirical treatment effect analysis.
11. In this case, propensity scores are estimated by means of a multinomial logistic model. The number of neighbours used to calculate the matched outcome is five and a calliper width of 0.20 standard deviations is used in most cases.