Supplementary Materials I: Some Methodological Remarks

Supplementary Materials I: some methodological remarks

Both the numerical simulations in the main text and the statistical analysis in Appendix III were performed based on the assumption of a representative individual at a country level. Indeed, the conditions required to perform such aggregation (i.e., combining all the data to provide a representative individual) are likely to be met, since moral environmental behaviours are often based on habit and mimic behaviours (i.e., emulating the behaviours of the society in which the individual lives), and in a democratic country, if the majority of the population is against a governmental policy (e.g., environmental protection), it will not support that political party in subsequent elections; conversely, if the majority of the population is uncomfortable with a given social status (e.g., the degree of inequality), it will support a political party that tries to improve that status in subsequent elections.

Thus, this section will discuss interactions between intentional and non-intentional individual behaviours, and the effects on the stability of pro-environmental behaviours. Indeed, comparisons of alternative moral rules in terms of effectiveness (i.e., capacity to achieve a given measured goal) require us to assume a similar stability of alternative future scenarios. Otherwise, the first and most effective moral rule A could be preferred even if it is unstable (i.e., individuals will move away from that behaviour after a short period, if some people stop following the rule), whereas the second-most effective moral rule B should be preferred since it is stable.

The focus on environmental moral behaviours (e.g., waste differentiation behaviour, purchase of green products) lets us distinguish strategic stability (i.e., individuals are likely to perpetuate the same environmental decision) from evolutionary stability (i.e., all individuals will converge on pro-environmental behaviours, apart from innovators), and supports the use of the game theory framework applied to preservation of the commons (e.g., Rommel et al., 2015), where barriers and catalysts to fostering pro-environmental behaviours (e.g., Quimby and Angelique, 2011) are linked to ethical principles. Table S2 presents individual pay-offs for four alternative couples of decisions in terms of all permutations of pro- and anti-environmental behaviour. In this table, the couples can be defined in terms of game theory: Reward, in which individuals cooperate to achieve a greater reward at the cost of some mutual sacrifice; Temptation, in which one individual makes a selfish choice to improve their reward; Sucker, in which an individual who chooses a pro-social response suffers for that choice; and Punishment, in which both individuals suffer from their selfish choices. Of course, these choices also depend on ethical values.

Table S2. Possible interactions between individual pro- and anti-environmental behaviours. Categories of response: T = temptation, R = reward, S = sucker, P = punishment.

Pro-environment / Anti-environment
Pro-environment / R, R / S, T
Anti-environment / T, S / P, P

In terms of payoffs, ethics is likely to affect the payoffs as follows:

• Punishment is large if consumer knowledge and awareness are small (e.g., individuals are not aware of the economic and social consequences of climate change): information campaigns could decrease P.
• Reward is small if consumers do not believe in the efficacy of their individual behaviour on the environment (e.g., if they feel marginal and irrelevant in coping with global environmental issues) or if they have a sense of fatalistic helplessness (e.g., if they feel they cannot do anything because it is too late): information campaigns could increase R.
• Reward is small if green consumerism is expensive (e.g., high prices of green products), if it requires sacrifice (e.g., longer walks to use public transportation), and if it implies mindfulness (e.g., remembering to bring their own bags to the market): waste facilities, market policies, and incentives (e.g., a tax credit for installing solar panels) could increase R.
• Temptation is large if social pressure and feeling accountable are small for the consumer (i.e., if they do not care about criticism from their neighbours): negative feedback about higher household energy consumption than that of the neighbours could reduce T.
• Temptation is large if public facilities are lacking (e.g., poor spatial distribution of waste recycling) or if green markets are non-competitive (e.g., high prices of green foods): waste facilities, market policies, bans (e.g., incandescent bulbs), or fines (e.g., a plastic bag fee in stores) could reduce T.
• Sucker is small if social responsibility or moral awareness are small or if social conformity is large (i.e., if the consumer feels stupid by behaving pro-environmentally, when other people do not): responsibility campaigns could increase S.

In terms of ethical values, responsibility to nature (i.e., β) is depicted by a larger R and a smaller T; distributive justice towards current and future generations in both developed and developing countries (i.e., ε and ζ, respectively) are depicted by a smaller P; responsibility to current generations in developing countries and future generations in both developed and developing countries (i.e., δ and γ, respectively) are depicted by a larger S and a smaller P, respectively.

Note that I have made the following simplifying assumptions:

• There is no time variable (i.e., this is a static analysis), so an individual chooses their behaviour forever rather than choosing one behaviour at one time and a different behaviour at other times. However, conformity and routine are relevant in this context.
• Feelings do not depend on the number of people the individual faces, otherwise S should be smaller, with a larger number of people behaving anti-environmentally when an individual behaves pro-environmentally. However, social pressures arising from ethical rules are likely to be relevant (i.e., a small temptation) in a small community (e.g., churches, neighbourhoods).
• There is no positive time discount factor, otherwise R would be small (i.e., benefits to the environment of an individual’s pro-environmental behaviour might be observed in the far future by future generations) and P would be large (i.e., costs to the environment of an individual’s anti-environmental behaviour might be observed in the far future by future generations). However, awareness of impacts to the environment would reduce the time discount factor to 0.

Note that I used a symmetrical payoff matrix (i.e., individual i is facing individual j) rather than an asymmetrical payoff matrix (i.e., individual i would be facing other individuals –i), since individual i faces other individuals rather than an average individual. In particular, I depicted the effects of positive social pressure (i.e., support for pro-environmental behaviour), depending on the proportion of the population who behave differently in temptation-reward couples, in a smaller T minus R, and negative social pressure (i.e., disapproval of anti-environmental behaviour), depending on the proportion of the population who behave differently in punishment-sucker couples, in a smaller P minus S. Moreover, consequentialist and non-consequentialist behaviours are combined in a single matrix, because behaviours change continuously in alternative contexts where the relative importance of utilities and values differ (Irlenbusch and Villeval, 2015). Note that here, I have defined “consequentialist” to mean the belief that an action is evaluated by its consequences rather than by its attributes. Finally, a repeated game based on this static matrix is not realistic (e.g., agent i should face the same agent j in implementing a pro- or anti-environmental behaviour), and this would reinforce the obtained insights (e.g., a pro-environmental behaviour will prevail if the time discount factor is small enough).

In terms of game solutions, dominant strategies lead to a couple of pro-environmental behaviours if T < R and P < S. There is mixed behaviour if T > R and P < S. There is a single behaviour if T < R and P > S, where a simple evolutionary dynamics based on a binomial chance to innovate (i.e., to behave differently from the equilibrium according to a fixed probability) would identify the attraction basin (i.e., the initial values of T minus R, and P minus S leading to the single long-run [anti, anti] equilibrium) in which anti-environmental behaviour will prevail (i.e., P minus S > R minus T), and the attraction basin (i.e., the initial values of T minus R, and P minus S leading to the single long-run [pro, pro] equilibrium) in which pro-environmental behaviour will prevail (i.e., P minus S < R minus T). In particular, individuals will follow an environmental moral rule (i.e., a [pro, pro] equilibrium and an [R, R] outcome) if they are consequentialist, by comparing the benefits from obeying the rule (e.g., public benefits for current and future generations, current approval by people inside the community) with the costs of obeying the rule (e.g., time-consuming implementation, state penalty if the rule is broken, current disapproval by people outside the community), and if the benefits are larger than the costs (Lange et al., 2014). Alternatively, if the benefits are smaller than the costs (Hobman and Fredericks, 2014), individuals will follow the same environmental rule if they are not consequentialist (Helm et al., 2018; De Dominicis et al., 2017; Klein et al., 2017; Culiberg, 2014), because secular laws or institutions or because ethical values suggest that behaviour.

In terms of movement towards equilibrium, Figure S1 depicts the impacts of ethics using arrows (i.e., a relatively larger horizontal motion to the left whenever ethics affects payoffs by reducing T minus R, and a relatively smaller vertical motion towards the bottom whenever ethics affects payoffs by reducing P minus S), with the starting point assumed to be in the top right quadrant (i.e., a single unsustainable behaviour).

Figure S1. Possible equilibria: P, punishment, S, sucker; T, temptation; R, reward. R minus P = 3. Dark red = Prisoner’s dilemma, light red = anti-environmental behaviour; blue = mixed actions, green = pro-environmental actions, dark yellow = a single coordinated action toward anti-environmental actions, light yellow = a single coordinated action toward pro-environmental actions.

In particular, environmental ethics can increase R, by making the prisoner’s dilemma less likely, and can reduce T minus R and P minus S by making protection more likely (e.g., norms and social dynamics during waste management, as in Gould et al., 2016; carrying reusable grocery bags related to environmentalist and non-environmentalist behaviours due to wanted and unwanted social identity, as in Brick et al., 2017; moral obligation during pro-environmental behaviours, as in Nguyen et al., 2016; and social norms and guests’ commitments related to reuse of hotels’ towels, as in Terrier and Marfaing, 2015). Moreover, laws (e.g., a private self-focus to reduce idling of engines for long periods while waiting at rail crossings, as in Meleady et al, 2017; norms for immediate situational circumstances to reduce use of guest towels during hotel stays, as in Reese et al., 2014; game-based approaches to reduce household electricity consumption, as in Ro et al., 2017; purchasing appliances with energy star labels, taxes or bans from the government, as in Sachdeva et al., 2015) and institutions (e.g., firms aiming to realize corporate environmental responsibility, as in Ruepert et al., 2017; environmental campaigns that distinguish environmental and financial benefits, as in van der Broek et al., 2017; and information campaigns to reduce constructive pessimism, as in Kaida and Kaida, 2016) can reduce T minus R and P minus S, thereby making protection more likely. Finally, once achieved, the sustainable equilibrium is likely to be stable because social pressures are likely to be relevant for environmental ethics (e.g., peer support, such as adopting photovoltaic cells if your neighbours did or reusing towels in hotels if other guests do, as in Sachdeva et al., 2015), and because individual behaviours are likely to be driven by habits (e.g., pro-environmental actions sustained over long time periods, as in Chatelain et al., 2018; spillover effects on water conservation if household behaviours are perceived to be similar, as in Kneebone et al., 2018; spillover effects across different pro-environmental behaviours, as in Carfora et al., 2017; spillover effects between pro-environmental behaviours if the resources required to perform them are perceived to be similar, as in Margetts and Kashima, 2017; and spillover effects from green purchasing to other low-cost behaviours, as in Lanzini and Thogersen, 2014).

In terms of the expected equilibrium, ethical rules are likely to produce mixed behaviours (i.e., some individuals behave pro-environmentally and some all individuals do not do that) if T minus R is positive and P minus S is negative, although incentives, taxes, fines or controls (i.e., a reduction of T) are likely to increase the percentage of people behaving pro-environmentally.

Note that many moral rules can be simultaneously observed at equilibrium, since different ethical rules can coexist within different communities, with support and reprobation could come from the reference community.

References

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Supplementary Materials II: the dataset

Table S1. The dataset used in the present analysis. Data refer to 2012 or data from the most recent available year if the 2012 data is unavailable. Missing data on environmental protection, R&D, and aid expenditures have been replaced by 0.01. Missing data on Gini coefficients and government debt as a percentage of GDP have been replaced by the average calculated for OECD and non-OECD countries.

OECD / Per capita Ecological Footprint (ha) / Per capita GDP (purchasing power parity basis, PPP) / Consumption expenditures
(% GDP) / Environmental protection expenditures (%GDP) / R&D expenditures
(% GDP) / Foreign aid expenditures
(% GDP) / Gini coefficient / Governmental debt
(% GDP)
Afghanistan / N / 0.8 / 1,899 / 104
Albania / N / 2.2 / 9,791 / 79 / 0.29 / 64
Algeria / N / 2.1 / 13,196 / 32 / 0.28
Argentina / N / 3.1 / 12,034 / 65 / 0.64 / 0.42
Armenia / N / 2.2 / 7,268 / 85 / 0.24 / 0.3
Australia / Y / 9.3 / 42,541 / 54 / 2.22 / 0.36 / 0.35 / 40
Austria / Y / 6.1 / 44,154 / 54 / 2.93 / 0.28 / 0.31 / 66
Azerbaijan / N / 2.3 / 15,888 / 40 / 0.22 / 6
Bangladesh / N / 0.7 / 2,715 / 74 / 0.32
Belarus / N / 5.1 / 16,907 / 47 / 0.67 / 0.26 / 10
Belgium / Y / 7.4 / 40,625 / 52 / 1.11 / 2.36 / 0.47 / 0.27 / 97
Benin / N / 1.4 / 1,794 / 71 / 0.43
Bhutan / N / 4.8 / 7,120 / 37
Bolivia / N / 3 / 5,793 / 60 / 0.16 / 0.47
Bosnia & Herzegovina / N / 3.1 / 9,344 / 81 / 0.27 / 0.34 / 49
Botswana / N / 3.8 / 14,004 / 55 / 0.25 / 0.6 / 20
Brazil / N / 3.1 / 14,971 / 61 / 1.13 / 0.53 / 60
Bulgaria / N / 3.3 / 15,731 / 65 / 5.27 / 0.6 / 0.08 / 0.36 / 22
Burkina Faso / N / 1.2 / 1,520 / 58 / 0.2 / 0.37
Burundi / N / 0.8 / 717 / 76 / 0.12 / 0.39
Cambodia / N / 1.2 / 2,795 / 81
Cameroon / N / 1.2 / 2,666 / 69 / 0.46
Canada / Y / 8.2 / 41,868 / 56 / 1.8 / 0.32 / 0.34 / 36
Central African Republic / N / 1.2 / 913 / 88
Chad / N / 1.5 / 1,961 / 66 / 0.43
Chile / 4.4 / 21,142 / 62 / 0.36 / 0.47 / 9
China / N / 3.4 / 11,017 / 37 / 1.91 / 0.06 / 0.42
Colombia / N / 1.9 / 11,840 / 61 / 0.22 / 0.53 / 65
Comoros / N / 1 / 1,355 / 104 / 0.45
Congo, Dem. Rep. / N / 1.3 / 641 / 73 / 0.08 / 0.42
Congo, Rep. / N / 0.8 / 5,698 / 36 / 0.49
Costa Rica / N / 2.8 / 13,589 / 66 / 0.56 / 0.49
Cote d'Ivoire / N / 1.3 / 2,753 / 66 / 0.42
Croatia / N / 3.9 / 20,183 / 60 / 0.75 / 0.32
Cuba / N / 2 / 19,462 / 54 / 0.41
Cyprus / N / 4.2 / 31,710 / 67 / 0.43 / 0.11 / 0.34
Czech Republic / Y / 5.2 / 28,307 / 49 / 3.22 / 1.78 / 0.12 / 0.26 / 41
Denmark / Y / 5.5 / 42,869 / 48 / 3.01 / 0.83 / 0.28 / 40
Dominican Republic / N / 1.5 / 11,528 / 75 / 0.46
Ecuador / N / 2.2 / 10,322 / 60 / 0.33 / 0.47
Egypt / N / 2.2 / 10,067 / 81 / 0.51 / 0.3
El Salvador / N / 2.1 / 7,718 / 93 / 0.03 / 0.42 / 52
Eritrea / N / 0.4 / 566 / 78
Estonia / Y / 6.9 / 25,287 / 51 / 2.82 / 2.12 / 0.11 / 0.33 / 3
Ethiopia / N / 1.1 / 1,234 / 72 / 0.42 / 0.33
Fiji / N / 2.9 / 7,550 / 68
Finland / Y / 5.9 / 39,489 / 55 / 3.42 / 0.53 / 0.27 / 42
France / Y / 5.1 / 37,224 / 56 / 1.09 / 2.23 / 0.45 / 0.33 / 67
Gambia, The / N / 1 / 1,570 / 77 / 0.13 / 65
Georgia / N / 1.6 / 7,869 / 73 / 0.41 / 33
Germany / Y / 5.3 / 43,035 / 56 / 1.09 / 2.87 / 0.37 / 0.31 / 44
Ghana / N / 2 / 3,659 / 60 / 0.38 / 0.42
Greece / Y / 4.4 / 24,816 / 70 / 2.16 / 0.7 / 0.13 / 0.36 / 148
Guatemala / N / 1.9 / 6,855 / 86 / 0.04 / 0.5 / 24
Guinea / N / 1.4 / 1,197 / 74 / 0.34
Guinea-Bissau / N / 1.5 / 1,349 / 90 / 0.51
Guyana / N / 3.1 / 6,349 / 90
Haiti / N / 0.6 / 1,585 / 98 / 0.41
Honduras / N / 1.7 / 4,548 / 79 / 0.57
Hungary / Y / 2.9 / 22,337 / 54 / 1.27 / 0.09 / 0.3 / 94
India / N / 1.2 / 4,861 / 58 / 0.83 / 0.35 / 51
Indonesia / N / 1.6 / 9,283 / 56 / 0.08 / 0.39 / 25
Iran / N / 2.8 / 16,549 / 49 / 0.33 / 0.41
Iraq / N / 1.9 / 14,624 / 55 / 0.03 / 0.29 / 25
Ireland / Y / 5.6 / 45,642 / 45 / 2.49 / 1.55 / 0.47 / 0.32 / 130
Israel / Y / 6.2 / 30,879 / 56 / 4.16 / 0.07 / 0.41 / 75
Italy / Y / 4.6 / 34,796 / 62 / 1.65 / 1.27 / 0.14 / 0.35 / 109
Jamaica / N / 1.9 / 8,405 / 85 / 138
Japan / Y / 5 / 34,988 / 59 / 3.21 / 0.17 / 0.33 / 185
Jordan / N / 2.1 / 11,340 / 74 / 0.43 / 0.34
Kenya / N / 1.1 / 2,670 / 78
Korea / Y / 5.7 / 31,901 / 51 / 4.02 / 0.14 / 0.32 / 34
Kyrgyzstan / N / 1.9 / 2,870 / 96 / 0.17 / 0.27 / 54
Lao PDR / N / 1.2 / 4,498 / 66 / 0.36
Latvia / N / 6.3 / 20,482 / 61 / 1.77 / 0.67 / 0.07 / 0.35
Lebanon / N / 3.8 / 16,574 / 89 / 0.32
Lesotho / N / 1.7 / 2,384 / 92 / 0.07 / 0.54 / 44
Liberia / N / 1.2 / 770 / 121 / 0.33 / 33
Libya / N / 3.7 / 22,560 / 17
Lithuania / N / 5.8 / 23,722 / 62 / 3.35 / 0.9 / 0.28 / 0.35
Luxembourg / Y / 15.8 / 88,159 / 32 / 1.29 / 1 / 0.34 / 30
Macedonia, FYR / N / 3.3 / 11,569 / 74 / 0.33 / 0.4
Madagascar / N / 1 / 1,374 / 88 / 0.07 / 0.43
Malawi / N / 0.8 / 750 / 89 / 0.46 / 41
Malaysia / N / 3.7 / 22,706 / 50 / 1.09 / 0.46 / 52
Mali / N / 1.5 / 1,484 / 72 / 0.58 / 0.33
Mauritania / N / 2.5 / 3,488 / 48 / 0.32
Mauritius / N / 3.5 / 16,651 / 74 / 0.18 / 0.36 / 37
Mexico / Y / 2.9 / 16,136 / 66 / 0.49 / 0.48 / 27
Moldova / N / 1.8 / 4,151 / 95 / 0.42 / 0.29 / 24
Mongolia / N / 6.1 / 9,809 / 53 / 0.24 / 0.34 / 46
Montenegro / N / 3.8 / 13,817 / 83 / 0.34 / 0.32
Morocco / N / 1.7 / 6,854 / 60 / 0.71 / 56
Mozambique / N / 0.9 / 992 / 78 / 0.38
Nepal / N / 1 / 2,115 / 78 / 0.3 / 0.33 / 34
Netherlands / Y / 5.3 / 45,728 / 45 / 3.03 / 1.94 / 0.71 / 0.28 / 68
New Zealand / Y / 5.6 / 32,806 / 59 / 1.19 / 0.28 / 0.33 / 66
Nicaragua / N / 1.4 / 4,388 / 82 / 0.11 / 0.45
Niger / N / 1.6 / 867 / 68 / 0.33
Nigeria / N / 1.2 / 5,310 / 58 / 0.43 / 10
Norway / Y / 5 / 63,620 / 40 / 1.62 / 0.93 / 0.26 / 26
Oman / N / 7.5 / 41,186 / 22 / 0.21 / 5
Pakistan / N / 0.8 / 4,380 / 82 / 0.31 / 0.31
Panama / N / 2.8 / 17,903 / 54 / 0.08 / 0.52
Papua New Guinea / N / 1.9 / 2,480 / 51 / 0.42
Paraguay / N / 4.2 / 7,312 / 71 / 0.09 / 0.48
Peru / N / 2.3 / 10,851 / 62 / 0.06 / 0.45 / 19
Philippines / N / 1.1 / 6,042 / 74 / 0.13 / 0.42 / 51
Poland / Y / 4.4 / 22,872 / 62 / 2.41 / 0.88 / 0.09 / 0.32 / 50
Portugal / Y / 3.9 / 25,953 / 66 / 0.65 / 1.38 / 0.28 / 0.36 / 88
Romania / N / 2.7 / 17,817 / 63 / 0.48 / 0.08 / 0.27
Russian Federation / N / 5.7 / 23,299 / 51 / 1.05 / 0.03 / 0.41 / 9
Rwanda / N / 0.9 / 1,483 / 79 / 0.51 / 51
Senegal / N / 1.2 / 2,184 / 76 / 0.54 / 0.4
Serbia / N / 2.7 / 12,505 / 77 / 0.91 / 0.29
Sierra Leone / N / 1.2 / 1,550 / 90 / 0.34
Singapore / N / 8 / 75,630 / 38 / 2.01 / 110
Slovak Republic / Y / 4.1 / 25,507 / 57 / 0.8 / 0.09 / 0.26 / 39
Slovenia / Y / 5.8 / 27,680 / 57 / 0.84 / 2.58 / 0.13 / 0.26 / 26
South Africa / N / 3.3 / 12,375 / 61 / 0.63 / 26
Spain / Y / 3.7 / 31,657 / 59 / 1.97 / 1.29 / 0.16 / 0.35 / 84
Sri Lanka / N / 1.3 / 9,981 / 65 / 0.12 / 0.39 / 69
Sweden / Y / 7.3 / 43,263 / 47 / 0.27 / 0.97 / 0.27 / 34
Switzerland / Y / 5.8 / 54,582 / 53 / 2.97 / 0.47 / 0.32 / 20
Syria / N / 1.5 / 5,436 / 59
Tajikistan / N / 0.9 / 2,343 / 119 / 0.11 / 0.3
Tanzania / N / 1.3 / 2,248 / 69 / 0.45
Thailand / N / 2.7 / 14,597 / 55 / 0.4 / 0.03 / 0.39 / 28
Timor-Leste / N / 0.5 / 2,038 / 67
Togo / N / 1.1 / 1,294 / 77 / 0.22 / 0.45
Trinidad and Tobago / N / 7.9 / 30,019 / 55 / 0.04
Tunisia / N / 2.3 / 10,535 / 66 / 0.68 / 45
Turkey / Y / 3.3 / 18,032 / 62 / 0.92 / 0.32 / 0.4 / 39
Turkmenistan / N / 5.5 / 12,460 / 15
Uganda / N / 1.2 / 1,666 / 77 / 0.47 / 0.41
Ukraine / N / 2.8 / 8,319 / 80 / 0.75 / 0.25 / 34
United Kingdom / Y / 4.9 / 36,765 / 66 / 2.41 / 1.61 / 0.56 / 0.32 / 101
United States / Y / 8.2 / 50,549 / 68 / 2.7 / 0.19 / 0.41 / 94
Uruguay / N / 2.9 / 18,439 / 67 / 0.33 / 0.41 / 43
Uzbekistan / N / 2.3 / 4,705 / 66 / 0.2
Venezuela, RB / N / 3.6 / 17,702 / 59
Vietnam / N / 1.7 / 4,910 / 63 / 0.28 / 0.36
Yemen, Rep. / N / 1 / 3,609 / 88 / 0.37
Zambia / N / 1.1 / 3,501 / 55 / 0.56
Zimbabwe / N / 1.4 / 1,649 / 93 / 0.43

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