Exploring energy transition in European firms: the role of policy instruments, demand-pull factors and cost-saving needs in driving energy-efficient and renewable energy innovations

ABSTRACT

The transition toward more efficient and sustainable energy systems relies on the introduction by firms of innovations aimed at reducing the energy-environmental impact. In this paper, controlling for firms’ technological capabilities and taking into account the role of specific policy instruments, demand-pull factors and firms’ cost-saving needs, we carry out an in-depth analysis of the drivers motivating firms’ decision to engage in two main types of energy innovations, energy-efficient and renewable energy innovations, as compared to other environmental technologies. The empirical evidence, provided by using firm-level data from the Community Innovation Survey for different European countries, highlights the key role of firms’ cost saving needs as a motivation driving the introduction of both types of energy innovations, as well as of governmental subsidies and public procurement though only for renewable energy innovations. The influence of demand-pull factors is instead transversal between energy innovations and other eco-innovations.

KEYWORDS:

• Energy-efficient innovation
• renewable energy innovation
• policy instruments
• demand and cost factors

Notes

¹ Based on CIS 2014 survey, industry dummies are constructed as follows: 1 = Agriculture, forest and fishing, Mining and quarrying; 2 Manufacture of food products, beverages and tobacco products, textiles, apparel, leather, wood, paper and printing; 3 = Manufacture of coke, refined petroleum products, chemicals, pharmaceuticals, rubber and plastics products, other nonmetallic mineral products, basic metals and fabricated metal products; 4 = Manufacture of computer, electronic and optical products, electrical equipment, machinery and equipment, transport equipment, other manufacturing, and repair and installation of machinery and equipment; 5 = Construction; 6 = Wholesale and retail trade; 7 = Transportation and storage, Accommodation and food services, Arts, entertainment and recreation; 8 = Information and communication; 9 = Financial and insurance activities, Real estate activities, Professional, scientific and technical activities, 10 = Public utilities (electricity, gas, steam and air-conditioning supply, Water supply, sewerage, waste management and remediation), Administrative and support service activities, Public administration and defense, compulsory social security, Education, Human health services, Residential care and social work activities, Other services.

² For Lithuania, there are no data available to construct the variable Education.

³ See Cappellari and Jenkins (2003).

⁴ For identification, sample selection models generally require at least one regressor in the selection equation to be excluded from the outcome equation. The exclusion restriction variable is often difficult to find because it should have a substantial impact on the selection but it should not directly affect the outcome, thus it can be reliably excluded from the outcome equation. In the CIS 2014 dataset we cannot find such variable. Thus, model identification is based upon the non-linearity of the functional form of the selection equation (see Cameron and Trivedi 2009, chapter 16). The same approach has been adopted by Marzucchi and Montresor (2017).