Technological Progress in the Transition to a Sustainable Economy

In order to address climate change and environmental pollution while minimizing the costs to society, sound environmental policies should be devised. This dissertation studies firms' clean investment and innovation decisions in response to policies and market circumstances. Chapter 2 investigates the energy efficiency (EE) gap, referring to private agents not making seemingly profitable investments to reduce energy use. We find that most firms (70%) have made EE investments in the past five years, and that the median firm has saved 10% of its energy use. The remaining profitable EE investment opportunities still leave room for another 15% of energy savings at the median firm. We find that uncertainty about future policies ranks as the leading barrier to EE investments, followed by lock-ins in current equipment, and energy price uncertainty. Chapter 3 studies the effects of the EU Emissions Trading System (EU ETS) on the economic performance and investments of Dutch manufacturing firms. We pay close attention to the staggered design of the ETS as well as to potential treatment effect heterogeneity. Our results align with those of the previous literature. Even when studying the more stringent third phase and when using estimators appropriate for the staggered ETS setting, there seems to be no discernible effects of the ETS on firms' economic performance. We also do not find any statistically significant effect on the investment behavior of regulated firms. Chapter 4 suggests a name matching algorithm that improves studies on the firm's patenting behavior. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps, namely (1) cleaning, (2) similarity scoring, (3) a decision rule based on supervised machine learning, and (4) group identification using community detection. The algorithm is applied to merging firms in the Amadeus Financials and Subsidiaries databases, containing firm-level business and ownership information, to applicants in PATSTAT, a worldwide patent database. For the application the algorithm vastly outperforms an exact string match by increasing the number of matched firms in the Amadeus Financials (Subsidiaries) database with 116% (160%). 25.5% (7%) of all patent applications (applicants) since 1950 involving firms are matched to firms in the Amadeus databases, compared to 3.6% (1.3%) for an exact name match. In Chapter 5 the database from Chapter 4 is used to study induced innovation and the influence of market competition. This chapter empirically investigates two drivers of clean innovation, energy prices and market competition, grounded in the induced innovation and inverted-U literature, respectively. I find evidence of the induced innovation hypothesis, but only when studying energy price changes, and not when studying energy price levels. I also consistently find an inverted-U relationship between competition and innovation, although the relationship is statistically uncertain. When combining the two hypotheses, the results are inconclusive. While the induced innovation findings are robust against different econometric specifications and estimation methods, they vary depending on whether energy prices are first-differenced or not.