Smudging and its Effects on Environmental Regulations

Empirical evidence indicates that industries worldwide are greatly expanding their sponsorship of research in an era when thousands of newly engineered products are being developed and marketed, and novel production processes implemented with insufficient regulatory oversight and little understanding of the long-term risks. The empirical evidence also shows that industry sponsorship of research is associated with outcomes that are favourable for the sponsor. By shaping the evidence about the risks generated by their products, an action that we refer to as “smudging”, industry groups are likely to slow or prevent their regulation.

Studies have shown, for example, that research sponsored by the pharmaceutical industry is more likely to yield results that benefit the company's product than studies funded by other sources. One historical example is the tobacco industry, which has invested considerable resources in seeking to attack and disprove scientific studies on the effects of passive smoking and thus initially had an impact on the design of regulations for the tobacco industry.

This project will investigate how industries seek to shape scientific evidence, and how regulators incorporate scientific evidence in their decisions on environmental regulation. Such information will enable us to understand how information bias and the shaping of information by interest groups can affect the probability to implement environmental regulations and the stringency of the regulations.

Machine learning to help review studies

The industry can influence results from scientific studies that they themselves finance in several different ways: the design of the study, the way in which the exposure takes place and the results that are evaluated. They can also avoid unfavorable research for the product and instead disseminate favorable research to decision-makers and the general public by circumventing the normal channels of scientific publication.

A limitation of studies that attempt to identify the occurrence of "smudging" is the difficulty of identifying studies that explicitly refer to industry-funded research. The studies are often based on relatively few observations and have not been able to evaluate methodological differences between studies funded by industry and those that are not. In this project, we will, among other things, use text-mining that uses machine-learning algorithms to investigate whether (and if so to what extent) the industry has influenced the design of environmental policy instruments by funding or conducting research whose results prove beneficial to the industry. We will quantitatively compare toxicological effects from studies conducted at the university and studies associated with the industry to examine whether the scientific quality of industry-related publications differs from those conducted at the university.

Examines decisions in five areas

We will also examine how authorities incorporate scientific knowledge into their decisions in five areas: chemicals, air pollution, overfertilization, transport and forestry. In particular, we will examine and quantify how decisions are affected by factors such as the variation in scientific results, whether scientific evidence comes from industry or academia, whether environmental problems affect vulnerable groups in society and the size of the expected action costs to achieve environmental goals.

To maximize the project's policy relevance, we will also make a synthesis where we use economic modeling to investigate the impact on environmental policy instruments in the presence and absence of smudging to analyze which environmental policy institutions and designs that can correct such market failures.