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Innovation for climate change adaptation and technical efficiency: an empirical analysis in the European agricultural sector

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Abstract

This paper analyses the effect of innovation on firms’ technical efficiency. Using climate-related patent data to proxy for innovation activity in different technological fields, the paper employs a stochastic frontier approach to estimate the impact of innovative efforts on agricultural firms’ technical efficiency taking account of both unobservable heterogeneity and double heteroscedasticity in the inefficiency and idiosyncratic terms. Our findings confirm that innovation has a positive impact on firms’ productivity (technical efficiency). While agricultural firms located in Germany and Sweden are more efficient compared to those in southern countries, all the European countries considered are distant from the maximum production frontier. This leaves room for governments to design economically sustainable agriculture policies, incentivize firms and foster technological innovation to achieve adaptations to present and future changes in climate.

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Notes

  1. A firm is considered to be technically efficient if it achieves its maximum output level based on the optimal combination of the inputs within its existing technology. The estimated production frontier coincides with the optimal output level and represents the benchmark against which firm-level efficiency is measured. The SFA approach allows the inclusion of exogenous variables which it is believed affect technical inefficiency.

  2. The requirement imposed on agricultural sector firms was that they must publish their balance sheets and, thus, partnerships, which excludes small firms. For a review of the pros and cons of using ORBIS data, see Ribeiro et al. (2010).

  3. The IPC is hierarchically structured and provides classification codes related to different levels of technological specificity.

  4. Similar to the IPC, the CPC provides information on the technological field of the invention. Most of the IPC and CPC codes, however, CPC includes a specific section for technologies related to climate change which allows identification of a range of technologies that include the vast majority of green technologies.

  5. The following IPC codes were used to identify the set of patents related to adaptation to climate change in agriculture: (i) A01H, related to new plants and processes for obtaining them; (ii) C12N 15/82, related to mutation or genetic engineering for plants cells; (iii) C12N 15/29, related to genes encoding plant proteins; (iv) C12N 15/05, related to the preparation of hybrid cells by fusion of two or more plant cells.

  6. Note that the values of the inefficiency scores stem not just from the determinants of inefficiency model, such as innovation indicators, but also from the inputs in the production function model. Indeed, inefficiency can be explained by observed (determinants of inefficiency model and input use) and unobserved (e.g., input allocation) effects. The implications are that, in many cases, the positive effect of innovation activities, although they increase technical efficiency (see our main result in Table 4), is not sufficiently sizeable to compensate for the reduced efficiency induced by other sources such as inefficient input allocation.

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Acknowledgements

We are indebted to the Editor and two anonymous reviewers for valuable comments on an earlier version of the manuscript. Part of this research was conducted while Nicolò Barbieri was on a research visit to SPRU—Science Policy Research Unit (University of Sussex). Nicolò Barbieri acknowledges financial support from the 5 × 1000 Fund for Young Researchers’ Mobility of the University of Ferrara.

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Authors and Affiliations

  1. Department of Political Science and International Relations, University of Palermo, Via Maqueda, 324, Palermo, Italy

    Sabrina Auci

  2. Department of Economics and Management, University of Ferrara and SEEDS (Sustainability, Environmental Economics and Dynamics Studies), Via Voltapaletto 11, Ferrara, Italy

    Nicolò Barbieri

  3. Economics Department, University of Rome “Niccolò Cusano”, Via Don Carlo Gnocchi, 3, Rome, Italy

    Manuela Coromaldi

  4. Environmental and Territorial Statistics Directorate, ISTAT, Italian National Institute of Statistics, Via C. Balbo, 16, Rome, Italy

    Donatella Vignani

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  1. Sabrina Auci
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Appendices

Appendix A

1.1 Logistic regression model

To estimate whether and to what extent innovation activity is affected by climatic variables, we developed a fixed effects panel logistic regression analysis. The aim was to model the relationship between a binary response variable (1 for at least one patent and 0 otherwise) and climatic factors such as maximum temperature and rainfall. More specifically, the probability to innovate, measured by owning at least one patent (response variable \({Z}_{it}\)) might follow a binomial distribution. Developing three different models allows us to estimate the relationships between the three patent indicators—agricultural, crop biotechnology or climate adaptation-related patents—and the annual average maximum temperature and total annual precipitation (the selected explanatory variables (\({x}_{it}\))). This relationship can be written as:

$$Pr\left({Z}_{it}=1|{x}_{it}\right)=F\left({\delta }_{i}+{\gamma }_{1}{Tmax}_{it}+{\gamma }_{2}{Tmax}_{it}^{2}+{\gamma }_{3}{Rainfalls}_{it}+{\gamma }_{4}{Rainfalls}_{it}^{2}\right)$$
(6)

As in Dell et al. (2014) and Schlenker and Roberts (2009), we allow flexible estimation of the nonlinear relationships between innovation and the climatic variables by introducing squared terms of the climatic variables. These models are fitted using conditional maximum likelihood where data are grouped, and the likelihood is calculated relative to each firm (See Tables 8, 9).

Table 8 Summary statistics of climatic variables
Full size table
Table 9 Logit estimation results
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Appendix B

See Table 10.

Table 10 TFE estimations on the four-year lagged model with heteroscedasticity among groups
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Auci, S., Barbieri, N., Coromaldi, M. et al. Innovation for climate change adaptation and technical efficiency: an empirical analysis in the European agricultural sector. Econ Polit 38, 597–623 (2021). https://doi.org/10.1007/s40888-020-00182-9

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