| Literature DB >> 32836836 |
Sandra Rousseau1, Nick Deschacht2.
Abstract
As our behavioral patterns change due to the COVID-19 crisis, our impact on nature and the environment changes too. Pollution levels are showing significant reductions. People are more aware of the importance of access to local green and blue spaces. By analyzing online search behavior in twenty European countries, we investigate how public awareness of nature and the environment has evolved during the COVID-19 crisis. We find that the crisis goes hand in hand with a positive shift in public awareness of nature-related topics, but that awareness of environmental topics remains unaffected. While the decreasing pollution levels and media attention may reduce the overall sense of urgency to tackle pollution problems, the increased experience with local natural resources may strengthen public support for a recovery program that puts the transition towards a more sustainable economic system centrally. © Springer Nature B.V. 2020.Entities:
Keywords: COVID-19; Environment; Google Trends; Nature; Public awareness
Year: 2020 PMID: 32836836 PMCID: PMC7354367 DOI: 10.1007/s10640-020-00445-w
Source DB: PubMed Journal: Environ Resour Econ (Dordr) ISSN: 0924-6460
Fig. 1GT search popularity indicator for daily searches of ‘coronavirus’.
Notes: Five-day moving average of the Google Trends indicator. Cross-country mean, weighted by population
Fig. 2Search behavior for topics related to nature and environment (January 1st, 2019—May 11th, 2020).
Data source: Google Trends. Notes: Three-week moving average of the Google Trends indicator. Mean across keywords (unweighted) and countries (weighted by population size)
Fig. 3The effect of the COVID-19 crisis on search behavior.
Notes: Mean Google Trends indicator across keywords (unweighted) and countries (weighted by population size)
The effect of COVID-19 on natural and environmental awareness (regression analysis)
| (1) | (2) | |
|---|---|---|
| Nature keywords | Environment keywords | |
| Coef./(SE) | Coef./(SE) | |
| Time: after March 14 (T) | 1.665*** | − .174 |
| (.272) | (.104) | |
| Treated: year 2020 (D) | .066 | − .019 |
| (.177) | (.076) | |
| DiD effect (DxT) | 4.138*** | .081 |
| (.563) | (.077) | |
| Country (20 categories) | Yes | Yes |
| Topic (6 categories) | Yes | Yes |
| Observations | 2400 | 2400 |
*p < .05; **p < .01; ***p< .001. The dependent variable is the Google Trends SPI. Linear regression with standard errors clustered at the country-level. Constant terms are included in each model
The effect of COVID-19 on nature and environmental awareness (regression analysis with a bandwidth of 2 weeks)
| (1) | (2) | |
|---|---|---|
| Nature keywords | Environment keywords | |
| Coef./(SE) | Coef./(SE) | |
| Time: after March 14 (T) | 1.575*** | − .148 |
| (.264) | (.098) | |
| Treated: year 2020 (D) | − .212 | − .031 |
| (.191) | (.076) | |
| DiD effect (DxT) | 4.076*** | .102 |
| (.562) | (.086) | |
| Country (20 categories) | Yes | Yes |
| Topic (6 categories) | Yes | Yes |
| Observations | 2880 | 2880 |
*p < .05; **p < .01; ***p < .001. The dependent variable is the Google Trends SPI. Linear regression with standard errors clustered at the country-level. Constant terms are included in each model
Estimated effects by topic
| DiD effect: Estim. coefficient | SE | N | |
|---|---|---|---|
| Nature topics | |||
| Biodiversity | .072 | (.104) | 400 |
| Birds | 7.800*** | (1.325) | 400 |
| Forest | 6.154** | (1.656) | 400 |
| Gardening | 3.682* | (1.613) | 400 |
| Vegetable garden | 5.175*** | (.808) | 400 |
| Nature | 1.945 | (.933) | 400 |
| Environment topics | |||
| CO2 tax | − .071 | (.080) | 400 |
| Air pollution | .695 | (.364) | 400 |
| Circular economy | − .076 | (.111) | 400 |
| Climate change | − .174 | (.189) | 400 |
| Noise pollution | .145 | (.072) | 400 |
| Water pollution | − .030 | (.079) | 400 |
*p < .05; **p < .01; ***p < .001. Each row presents the estimated coefficient of the DiD term (DxT) in a linear regression model with the SPI for the topic in this row as the dependent variable. Each model includes a constant term, treatment and time indicator variables and country fixed effects. Standard errors clustered at the country-level