Environmental Law is predicated on the assumption that the government can effectively motivate its citizens to perform or abstain from certain activities. The question is how incentives and punishments should be used to achieve and ensure the greatest effect. There is evidence to suggest that excessive reliance on the threat of punishment alone is not only insufficient but counterproductive. Furthermore, without having clear metrics, it is unreliable to use any mixture of deterrent or incentive. This is especially true for the transportation sector.
The US EPA and other state and local regulatory agencies do not have sufficient resources to thoroughly police all individuals and organizations to ensure total compliance. As a consequence, many individuals and organizations intentionally avoid compliance often through deceptive means. On the smallest level, it is not uncommon for individuals in California to cheat on smog tests as evidenced by internet forums discussing how to get around smog checks. On a larger scale, there was the major scandal with Volkswagen cheating US EPA regulations by programming their vehicles to detect drive cycles and adjust engine performance to produce a different emission pattern than regular driving. People ex rel. Madigan v. Volkswagen Aktiengesellschaft. If it was not for independent research, this scandal may never have been exposed.
Why do these occur? In large part, it is because the regulatory and economic circumstances converged to incentivize individuals and organizations to take the risk of non-compliance. The risk of non-compliance appeared less likely than the reward. When government regulations produce perverse incentives they need to be restructured if they are going to be effective. Does that mean harsher penalties? Perhaps not. B.F. Skinner discovered with a variety of organisms that the threat of punishment is less effective than using various schedules of positive reinforcement. Further studies in management have revealed there are ideal ratios of positive reinforcement and criticism. There is further evidence of this with the adoption of electric vehicles due to government incentives.
Any approach should involve meticulously collecting and analyzing quantifiable data to demonstrate with a reasonable degree of statistical probability if a given regulation or incentive is actually achieving its intended effect. In many respects, the relative success of the Clean Air Act was the insistence on quantifiable data. Another aspect of the CAA was the tangibility of what they were trying to avoid. It is much easier to grasp why thick smoke is harmful to health than why invisible emissions are causing climate change. More so than other pollutants proposals to reduce greenhouse gas emissions must involve the development and adoption of technology. That necessarily means any Environmental Law must correctly produce the ratio of punishment to the incentive.
In the 1990s President Clinton initiated the Clean Car Program to spur green innovation within the automotive industry. This led to the EPA developing technology with the public sector in public partnerships through their Clean Automotive Technology program. Despite some of this technology being commercialized, the program was eventually terminated. The primary reason for this shutdown was because of greater regulatory power during the Obama administration. What was the point of using taxpayer dollars to help companies innovate when the government could just force them to increase fuel efficiency? I argue that kind of thinking is what lead to the disaster with VW. It also enabled the current administration to shut out valuable research for establishing fuel economy standards.
Automakers face enormous constraints from both the government and the market. Government threats are crippling, market threats are existential. The data indicates many of these regulations resulted in millions of vehicles are produced which are less popular. The total environmental effect of creating unused vehicles is arguably more catastrophic.
A better way to draft an effective policy is to approach the problem from a multi-disciplinary approach and have concrete metrics associated with each discipline. The problems that policy attempts to solve involve just as much complexity, if not more, than the vehicles that those policies are meant to regulate. Every vehicle is composed of thousands of parts from a long supply chain of plastics, metals, electronics, aerodynamics, material science, and more. An inaccurate model will lead to a vehicle that cannot drive or will quickly fall apart. Likewise, for the policy, there need to be network models that involve at minimum variables from disciplines of psychology, sociology, game theory, macroeconomics, supply-chain, and manufacturing. Yet, most of the policies that are introduced reflect a model of governance that still has not grappled with the vast potentials of data science and machine learning.
At present many policies appear to be drafted from a simplistic view of wanting to achieve a simple metric, say increase fuel-economy, through bureaucratic enforcement and incentives. A metric that ignores the greater environmental harm that may be occurring as a consequence of attainment or nonattainment. For the policy of the future to be effective it must be as sophisticated as the reality it hopes to regulate and as precise as the goal it seeks to achieve.
The startup world has demonstrated customer discovery is essential. Virtually any product is doomed to fail, no matter how innovative or sophisticated, without first talking to the end-user. A more sophisticated technical model combined with a willingness to actually speak with the people these policies aim to influence is where it all comes together.
Ultimately policy should be crafted using sophisticated models with tangible metrics that recognize the incentives across multiple domains through conversations with the people it aims to regulate and motivate. Without a robust balance of interpersonal communication and statistical analysis, we risk not only failing to achieve our goals but potentially making things worse.

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