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A master's thesis from Aalborg University
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The Day After Tomorrow: Climate Mitigation, The Harm Principle and Legitimate Legal Coercion: Climate Mitigation through Legal Coercion

Author

Term

4. term

Publication year

2021

Submitted on

Pages

79

Abstract

Denne afhandling undersøger, hvilke former for retslig tvang der kan retfærdiggøres for at nedbringe brugen af dyrelandbrug med det formål at begrænse klimaforandringer. Jeg anvender Joel Feinbergs skadeprincip, som siger, at lovgivning kan begrænse handlefrihed, når det er nødvendigt for at forhindre væsentlig skade på andre. Jeg udvider forståelsen af skade ved hjælp af Simester og von Hirschs begreb om ikke-standard skade, altså skader der er indirekte, kollektive eller diffuse – som klimaskader typisk er. Hvis dyrelandbrug i væsentlig og relevant grad skader andre ved at forværre klimaforandringer, kan retslig tvang derfor være berettiget. Jeg drøfter også legal moralisme som modposition til skadeprincippet for at afgrænse og styrke min hovedtilgang. Afhandlingen skelner mellem forskellige landbrugskontekster – illustreret ved figurerne “Old McDonald” og “Farmer Faisil” – for at vise, at bidraget til global opvarmning og mulighederne for at ændre praksis varierer efter geografi, økonomi og samfundsforhold. Derfor bør retslige tiltag tilpasses lokale forhold. Jeg gennemgår et spektrum af virkemidler fra de mest indgribende til de mindst, eksempelvis fra forbud og stramme kvoter over afgifter og reguleringer til tilskud, vejledning eller informationsindsatser. Konklusionen er tredelt: For det første skal retslig tvang stå mål med den skade, dyrelandbrug forårsager – den må ikke skabe nye, urimelige skader. For det andet kan alle landmænd ikke behandles ens; tiltag skal tage højde for geografiske, økonomiske og sociale forskelle. For det tredje er klimaforandringer og afbødning komplekse og rummer mange hensyn, men dyrelandbrug forvolder en betydelig skade, som bør forebygges. Retslig tvang kan både forhindre “Old McDonald” i at bidrage yderligere og forpligte ham til at indføre afbødningsstrategier, og den kan støtte “Farmer Faisil” i at optimere sin drift, så han ikke forværrer klimaforandringerne.

This thesis examines which forms of legal coercion are justified to reduce the use of animal agriculture in order to mitigate climate change. I apply Joel Feinberg’s Harm Principle, which holds that the law may limit freedom to prevent significant harm to others. I broaden the notion of harm using Simester and von Hirsch’s concept of non-standard harm—indirect, collective, or diffuse harms typical of climate impacts. If animal agriculture harms others in a substantial and relevant way by worsening climate change, legal coercion can therefore be justified. I also consider legal moralism as a counterpoint to the Harm Principle to clarify and strengthen the main argument. The analysis distinguishes between different farming contexts—illustrated by the figures “Old McDonald” and “Farmer Faisil”—to show that contributions to global warming and the capacity to change practices vary by geography, economics, and social conditions. Legal measures should thus be tailored to local circumstances. I discuss a spectrum of instruments from more to less intrusive, for example ranging from bans and strict quotas to taxes and regulations, and further to subsidies, guidance, or information efforts. The conclusions are threefold: First, any legal coercion must be proportionate to the harm caused by animal agriculture and should not create new, undue harms. Second, not all farmers can be treated the same; measures must reflect geographic, economic, and social differences. Third, climate change and mitigation are complex and involve many interests, but animal agriculture causes significant harm that should be prevented. Legal coercion can both stop “Old McDonald” from adding to the problem and require him to adopt mitigation strategies, and it can help “Farmer Faisil” optimize his farm so he does not further contribute to climate change.

[This summary has been rewritten with the help of AI based on the project's original abstract]