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Interaction between mitigation and adaptation is a key question for the design of climate policies. In this paper, we study how land use adaptation to climate change impacts land use competition in the agriculture, forest and other land use (AFOLU) sector and how a mitigation policy in agriculture might affect this competition. We use for this purpose two sector-specific bio-economic models of agriculture and forest combined with an econometric land use shares model to simulate the impacts of two climate change scenarios (A2 and B1, 2100 horizon), and a greenhouse gas emissions from agriculture policy consisting of a tax of between 0 and 200 €/tCO 2 equivalent. Our results show that both climate change scenarios lead to an increase in the area devoted to agriculture at the expense of forest which could have a negative impact on reducing greenhouse gas emissions responsible for climate change. The mitigation policy would curtail agricultural expansion, and thus could counteract the effects of land use adaptation to climate change. In other words, accounting for land use competition results in a reduction of the abatement costs of the mitigation policy in the agricultural sector. [ABSTRACT FROM AUTHOR]

• French people have limited knowledge about climate change but worry about it. • They prefer green investments or regulations to a tax and dividend. • Distributive effects and lack of alternatives prevent carbon tax acceptance. • Improving knowledge is predicted to increase ecological attitudes and concern. • Unlike in the US, partisanship does not interact with education to shape perceptions. This paper aims to assess the prospects for French climate policies after the Yellow Vests crisis halted the planned increase in the carbon tax. From a large representative survey, we elicit knowledge, perceptions and values over climate change, we examine opinions relative to carbon taxation, and we assess support for other climate policies. Specific attention is given to the link between perceptions of climate change and attitudes towards policies. The paper also studies in detail the determinants of attitudes in terms of political and socio-demographic variables. Among many results, we find limited knowledge but high concern for climate change. We also document a large rejection of the carbon tax but majority support for stricter norms and green investments, and reveal the rationales behind these preferences. Our study entails policy recommendations, such as an information campaign on climate change. Indeed, we find that climate awareness increases support for climate policies but no evidence for the formation of opinions through partisan cues as in the US, suggesting that better access to science could foster support for climate policies. [ABSTRACT FROM AUTHOR]

Abstract: Predictions of future land use areas are an important issue as land use patterns significantly impact environmental conditions (biodiversity, water pollution, soil erosion, and climate change) as well as economic and social welfare. In order to improve the prediction accuracy of aggregated land use share models, we propose in this paper a methodological contribution by controlling for both unobserved individual heterogeneity and spatial autocorrelation. Our model is a land use shares model applied to aggregated data in France. Our dataset is a panel which covers both time series observations from 1992 to 2003 and cross-sectional observations by Département (equivalent to NUTS3 regions). We consider four land use classes: (1) agriculture, (2) forest, (3) urban and (4) other use. We investigate the relation between the areas in land in different alternative uses and economic and demographic factors influencing land use decisions. Based on the comparison of prediction accuracy of different model specifications, our findings are threefold: First, controlling for both unobserved individual heterogeneity and spatial autocorrelation outperforms any other specification in which spatial autocorrelation and/or individual heterogeneity are ignored. Second, accounting for cross-equation correlations does not seem to improve the prediction performances and finally, ignoring individual heterogeneity introduces substantial loss in prediction accuracy. [Copyright &y& Elsevier]