Abstract

Wine grape production is very susceptible to weather conditions, with climate change presenting significant risks to the industry. Econometric analyses can offer valuable insights into the effects of past weather and climates, and of potential future climate changes. While econometrics is not the only method for deriving such insights, there is considerable potential for its application, especially given the rich (often underutilized) data sets available to the wine industry. Our article offers recommendations on how to effectively use econometrics to assess climate impacts on the wine industry. These recommendations are tailored to the specific characteristics of grape and wine production. We first discuss how to apply econometrics to assess the influence of weather or climate on wine-related outcomes. We provide insights into how to model weather and climate, avoid omitted variable bias, and address other modeling challenges. Finally, we explain how estimates of weather or climate impacts can be used to assess the potential effects of future climate change on the wine industry, providing valuable insights for the development of wine grape growers’ adaptation strategies.

Keywords: grape growing, wine production, weather, climate change, physical risks, adaptation

1. Introduction

The weather has a prominent role in grape growing and hence in the yield and quality of wine produced (Geppert et al., 2024). While climate change may have a positive economic impact in cooler regions (Ashenfelter & Storchmann, 2010, 2016), the opposite may be true for warmer regions. Most of the world’s wine production takes place in regions that are often too warm to produce high-quality wine from the most widely planted grape varieties (Puga, Anderson, Jones, et al., 2022). In this context, econometric studies assessing climate impacts can help guide policies and strategies for winegrowers to adapt to future changes in climates.

Econometrics is not the only approach to assessing climate impacts on the wine industry. Recent machine learning models are often useful for this purpose thanks to their predictive capabilities (Maimaitiyiming et al., 2019). Techniques that combine inference and prediction (see Daoud & Dubhashi, 2023) offer the potential for wine economists to provide high-quality climate change impact assessments. Crop simulation models can incorporate environmental factors rarely observed in actual growing conditions that are difficult to model with other types of statistical analyses (Antle & Stöckle, 2017).1 Experiments are also useful for identifying the impact of climate variables. Another, less data-driven approach consists of relying on the opinion of experts.2

Econometric analyses have the advantage of relying on data from actual wine-growing conditions, capturing growers’ actions and genuine responses to climatic circumstances. These conditions can differ from those in controlled settings (Blanc & Reilly, 2017). Given the wealth of data often available for the wine industry, it is possible to econometrically estimate the effect of weather or climate on an outcome. These models can lead to useful insights for the wine industry, often at a lower cost than when using other approaches such as field experiments. However, applying econometrics to quantify climate impacts on the wine industry requires considering a few aspects specific to the industry.

The aim of this article is to outline how to best use econometrics to assess climate impacts on the wine industry. More specifically, we recommend how to estimate the impact of weather, climate, and future climate changes on grape and wine production.3 In doing so, we do not intend to update past surveys of the impacts of climate change on wine-related outcomes, such as that by Ashenfelter and Storchmann (2016), nor analyze how climate change is and will be impacting the industry (see Santos et al., 2020, and van Leeuwen et al., 2024, for reviews on that topic).

The structure of the rest of this article is as follows. Section 2 explains how to use econometrics to estimate the effect of weather or climate on a wine-related outcome. Section 3 provides important considerations when using those estimates to quantify future impacts of climate change. Last, Section 4 concludes.

2. Estimating the Impact of Weather or Climate

It is possible to estimate the impact of weather or climate on a wine-related outcome by regressing that outcome on a set of weather or climate variables. Weather refers to atmospheric conditions during a short period, such as a growing season. Climate is the long-term average of those conditions over an extended period, often spanning decades.

The dependent variable of the econometric model is an outcome of interest. Examples of wine-related outcomes that are common in the wine literature are grape yields (e.g., Puga et al., 2023), grape production (e.g., Quiroga & Iglesias, 2009), wine production (e.g., Niklas, 2018), wine prices or quality (e.g., Ashenfelter, 2012, and Oczkowski, 2016), increases in alcohol concentration (e.g., Alston et al., 2011, and Godden et al., 2015), and advancements in harvest dates (e.g., Jarvis et al., 2019).

Such an econometric model can be estimated with cross-sectional, panel, or time series data. While a few interesting studies rely on very long time series data sets, often spanning centuries (e.g., Bock et al., 2013), most studies rely on either cross-sectional or panel data. That is also the case in the broader climate econometrics literature.

2.1. Choosing Weather or Climate Variables

Viticulture is affected by a wide range of weather variables that can have different effects throughout the year. Table 1 provides a very simplified summary of how the weather interacts with grapevine development and the timing of those developments (i.e., phenological growth stages). Yet even trying to account in a simplified way as to how weather affects the yield and quality of grape production can lead to a large number of independent variables in an econometric model. This is why most studies in the climate econometrics literature rely on just a few weather or climate variables. Also, including many correlated independent variables can make it difficult to interpret their coefficients because these variables are likely to absorb explanatory power from each other.

Table 1. Positive and negative weather influences on grapevine development and phenological growth stages.

^{Note. Adapted from} Jones et al. (2012) ^and van Leeuwen et al., (2024)^.

The appropriate weather variables often depend on the region. Indeed, the expected effect of the different weather variables can be subject to various characteristics of the region, including its climate. For example, the impact of higher temperatures has long been associated with higher quality wines in temperate regions such as Bordeaux (Ashenfelter, 2012; Ashenfelter et al., 2009), but the opposite effect is expected in most hotter wine regions such as in Australia (Puga, Anderson, & Doko Tchatoka, 2022). Animation 1 shows a climatic classification of the world’s wine regions by Puga, Anderson, Jones, et al. (2022). The variety of climates across regions, sometimes within the same country, can make it difficult to come up with an econometric model appropriate for all the regions within a study.

Besides depending on the region, the expected impact of weather or climate on an outcome often depends on the grape variety. Figure 1 shows the optimal growing season temperature range for producing high-quality wine of some of the most widely planted varieties, according to Jones et al. (2012). While there is debate about these temperature ranges (van Leeuwen et al., 2013), this figure gives an indication of how much the impact of weather on grape production differs across varieties.

Discriminating weather or climate impacts by region or variety is often difficult, as this would require a very large number of observations. Such large numbers are common for some broadacre crops, but are not commonly available in grape and wine data sets.

A widely used functional form for capturing nonlinear weather or climate effects implies adding a squared term to a weather or climate variable of interest. This leads to different estimated marginal effects of that variable, depending on the value of the variable itself. For example, an increase in the value of growing season temperature can change from being beneficial in cool regions to being detrimental in hot regions.

When using panel data, a perhaps more interesting approach consists of estimating the effect of exposure to different levels of temperature. Schlenker and Roberts (2009) pioneered this approach to estimate the effect of temperature exposure to different temperature intervals on the yield of broadacre crops, showing that yield increases gradually as temperatures rise but decreases sharply once temperatures exceed about 30° C. This specification allows for capturing the impact of extreme temperatures and can be used in wine applications. Smith and Alston (2024) employ a method that quantifies the impact of growing degree days at various temperature thresholds, enabling a more accurate assessment of the effects of temperature extremes.

By assuming that temperature follows a sine curve between the minimum and maximum daily temperature, it is possible to estimate similar models as long as there is information on minimum and maximum daily temperature (Ortiz-Bobea, 2021). That leads to the computation of a growing degree days variable that is different from those often used in wine research, which are based on the difference between the average daily temperature and a cutoff value (e.g., 10° C). More recently, Ortiz-Bobea et al. (2019) introduced a method to look at different effects of temperature throughout the growing season.

In addition to the above-mentioned semi-parametric models, there have been more recent developments using nonparametric functions. For instance, Schuurman and Ker (2025) estimate nonparametric models to account for nonlinear effects of weather across the yield distribution. Machine learning techniques such as neural networks can be useful for this purpose. Some of these methods, however, may require sample sizes that are uncommon in the wine industry.

That said, one benefit of wine-related data is that information is commonly available at the varietal level. In such instances, it becomes feasible to estimate a model in which the dependent variable is the outcome by variety, region, and year, rather than simply the regional outcome by year. As well, there are many wine databases with large sample sizes. This leads to a large number of observations, hence more degrees of freedom.

2.2. Avoiding Omitted Variable Bias

There are other variables that influence wine-related outcomes in addition to weather or climate variables. When using cross-sectional models, it is important to account for those other relevant variables to avoid biased estimates of the impact of climate on an outcome. The Ricardian model, first proposed by Mendelsohn et al. (1994), provides a theoretical framework that can be used for partially dealing with omitted variable bias in cross-sectional models (see Mendelsohn & Massetti, 2017, for a review). This type of model has been used in wine applications when looking at the impact of climate on vineyard prices (e.g., Cross et al., 2011, 2017).

There are other ways of decreasing the potential for omitted variable bias in a cross-sectional model. For example, Puga, Anderson, and Doko Tchatoka (2022) use a survey on 103 viticultural practices of various wine regions of Australia to assess more precisely the impact of temperature on grape prices, by accounting for those practices.

By contrast, panel data models include fixed effects to account for certain unobserved factors that might otherwise bias the results (Deschênes & Greenstone, 2007). The basic idea behind fixed effects is that, by controlling for characteristics that do not vary by unit of observation (e.g., region) or by time (e.g., year), it is possible to identify the impact of the variables of interest more accurately.

For example, in a panel data model in which the unit of observation is a region, the region fixed effects attempt to capture all time-invariant observable and unobservable characteristics of each region. That includes the time-invariant component of the climate.4 As a result, assuming the weather is appropriately represented, there is often less potential for omitted variable bias than in cross-sectional models.

However, omitted variable bias could still persist in panel data models thanks to time-varying variables. It is possible to control for those time-varying variables in the model, but these variables should be strictly exogenous (Dell et al., 2014). For example, if grape yield is the outcome variable, controlling for irrigation may be problematic because the season’s weather influences how much growers irrigate their vineyard. Instead, controlling for other related variables such as water prices may sometimes be preferable.

Adding time fixed effects to panel data models allows for a further decrease in the potential for omitted variable bias. These fixed effects account for time shocks that affect all regions in a given year. While there is still potential for omitted variable bias due to region-specific time-varying variables, this potential is lowered by using time fixed effects.

It may be tempting to interact groups of regions with time fixed effects as a way to account for time shocks that are common to a group of regions. However, these group-specific fixed effects can absorb a great amount of the weather variance, so that the residual variance in the data consists primarily of noise (Fisher et al., 2012). Weather data are commonly subject to measurement error. This error is least when the focus is restricted to the specific area in which the vineyards are planted.

2.3. Other Modeling Issues

There are other important econometric considerations in addition to the ones mentioned above.

One such consideration implies choosing whether to estimate variables as levels or as logs. In the climate econometrics literature, the dependent variable is usually specified in its natural logarithmic form. Such a specification has advantages such as mitigating issues of heteroskedasticity, and handling outliers or extreme values, by reducing the variable’s range (Wooldridge et al., 2021). Perhaps more importantly, this specification implies that the weather variables have the same proportional impact on a wine-related outcome across observations (e.g., regions).

While most studies model the weather or climate variables in levels (Camargo & Hsiang, 2015; Hsiang, 2016), it may be preferable to model the weather variables in their natural logarithms in some cases. For instance, when it seems more reasonable to assume constant elasticities for a nonnegative weather variable.

When working with panel data, another important econometric consideration involves choosing whether to estimate a dynamic model, that is, one incorporating a lagged dependent variable as an independent variable, rather than a static model. When estimating yields, most studies do not include the lag of this variable in their models. An exception is Chavas et al. (2019), who argue that a dynamic approach is justified because of the dynamics of crop fertility and management. Since grapevines are perennials, weather can influence grape production in more than one season (Molitor & Keller, 2017), adding another reason that might justify a dynamic model. However, modeling this is difficult due to the complex ways in which carbon is moved and allocated throughout the vine over the years.

In other cases, such as when modeling grape or wine prices, choosing whether to add a lag of the dependent variable can be more straightforward. The inclusion of a lag of grape or wine price in a model may be justified because the previous and current year prices can be linked through year-to-year changes in wine stocks.

To avoid biased estimates in dynamic models, it is possible to use the system generalized method of moments (system GMM) estimator developed by Arellano and Bond (1991). This is an example of a case in which other estimators may be more appropriate than the fixed effects estimator, which is the most commonly used estimator in panel data models (Blanc & Schlenker, 2017).

The estimation strategy and choice of estimator are also very relevant and can influence the way the estimates should be interpreted. When estimating a cross-sectional model using average weather (or climate data), the impact of climate is estimated. Instead, when using panel data, what is estimated is the impact of weather. If the model is estimated using the fixed effects estimator (the commonly used estimator in panel data models), what is estimated is the impact of weather shocks (Blanc & Schlenker, 2017). These denote differences from the mean weather, and they are considered random and exogenous.5

Another important econometric consideration involves dealing with spatial autocorrelation. When the data are available by wine regions, which are well delimited and distanced from each other, there may be less need to account for spatial autocorrelation. Still, even in those cases, not accounting for this type of autocorrelation could result in overly confident estimates. Ortiz-Bobea (2021) provides a well-grounded discussion on how to deal with spatial autocorrelation in climate econometrics.

3. Quantifying Future Impacts of Climate Change

The estimates of the impact of weather or climate from an econometric model can be used to quantify the potential consequences of climate projections. This implies using past observations to predict how climate change will affect a wine-related outcome in the relatively distant future. Therefore, the main limitation of this approach is that it assumes a ceteris paribus scenario, that is, one in which only the climate is considered to change.

Beyond the uncertainty related to variables that could change in the future other than climatic ones, there is statistical uncertainty surrounding the estimates and also uncertainty in the climate change projections. To deal with this last type of uncertainty, using different climate change projections and emission pathways is good practice (Burke et al., 2015).

A potential issue involves extrapolating outside the range of values observed in the data set. Puga et al. (2023) show that in their data set some of the climate projections for the end of this century (the orange diamonds in Figure 2) are too different from those in their data (the black circles), but that does not seem to be a problem with the midcentury climate projections (the green squares). Hence those authors focus only on the midcentury climate forecasts.

More uncertainty relates to the differences between what data are observed historically and what are not observed but may occur in the future. Part of that uncertainty is difficult to avoid. An example is the impact of carbon fertilization, which cannot be accounted for since the future levels of carbon dioxide (CO₂) in the atmosphere are expected to be higher than at present (and in the past).

Another example of an issue that may not be recorded in the data relates to increasingly common climatic events. This is arguably more relevant for grapevines than for annual crops because of the perennial characteristics of grapevines. Since yields form over two consecutive seasons, the weather in one season influences both the current and the following season (Guilpart et al., 2014). For instance, two consecutive years of drought may have a greater negative impact than two droughts of equal severity spread over nonconsecutive years.

Using the estimates of a panel data model usually raises another important issue: not accounting for the impact of long-run adaptation of already available technologies. This is because a panel data model estimates the impact of weather shocks on an outcome. Winegrowers change practices such as irrigation and winemaking techniques based on each season’s weather. Those short-run adaptation options are captured in the estimates of the impact of weather shocks, but what is not captured are the changes in production strategies resulting from long-run changes in climates. Some of those adaptations will likely take place in the vineyards and wineries (Santos et al., 2020).

In the case of wine, long-run adaptation might be less important due to the strong associations between grape varieties and production characteristics with specific regions often identified by geographic indications (GIs), such as protected designations of origin (PDOs) in Europe. These designations not only protect the product’s reputation but can regulate the production technology as well (Meloni et al., 2019). As a result, wine production is less flexible than for many other crops, both in terms of where it takes place and how it is produced, even over the long run, primarily due to marketing considerations.

That said, in recent years, researchers have developed hybrid methods that rely on the cross-sectional features of panel data sets for capturing long-run adaptation (see Kolstad & Moore, 2020, for a review). These approaches include long differences (e.g., Moore & Lobell, 2015), partitioning variation (e.g., Moore & Lobell, 2014, and Burke & Emerick, 2016), and panels with heterogeneous marginal effects (e.g., Butler & Huybers, 2012). Despite the possibility of getting plausible results without taking long-run adaptation into account in the wine sector, these recently introduced hybrid methods could lead to powerful insights.

4. Conclusion

Econometric models can provide estimates of the effect of weather or climate on a wine-related outcome. Those estimates can be used to assess the potential impact of future climate changes. Importantly, econometric models should consider the specific nature of grape and wine production. Wine industry–specific considerations should also influence the way the estimates are used to assess future climate risks. While this article provides an incomplete list of considerations when assessing climate impacts, it provides some key insights into how to approach this issue.

We recognize that econometrics is not the only approach for assessing climate impacts on the wine industry. However, econometric analyses have numerous advantages over other approaches, such as capturing winegrowers’ responses to climatic events. As well, many unexploited (often freely available) data sets can be used for econometric analyses of climate impacts. These analyses can be very helpful in shedding light on the impacts of weather and climate on the wine industry, and thereby in designing appropriate climate adaptation strategies.

Acknowledgments

Germán Puga and Kym Anderson are grateful for helpful comments from the Founding Editor-in-Chief Professor Xiao-Li Meng, Guest Editor Professor Karl Storchmann, and three anonymous reviewers, as well as from attendants at a seminar at UC Davis in February 2024, the Harvard Data Science Review Vine to Mind Symposium at Harvard University in June 2024, the 6th Wine and Hospitality Workshop at KEDGE Business School in June 2024, and the 16th Annual Conference of the American Association of Wine Economists at the EHL Hospitality Business School in July 2024.

Disclosure Statement

Germán Puga and Kym Anderson have received financial support from Wine Australia, under Research Project UA1803-3-1, and from the University of Adelaide’s School of Agriculture, Food and Wine and its Faculty of Arts, Business, Law and Economics.

References

Alston, J. M., Fuller, K. B., Lapsley, J. T., & Soleas, G. (2011). Too much of a good thing? Causes and consequences of increases in sugar content of California wine grapes. Journal of Wine Economics, 6(2), 135–159. https://doi.org/10.1017/S1931436100001565

Antle, J. M., & Stöckle, C. O. (2017). Climate impacts on agriculture: Insights from agronomic-economic analysis. Review of Environmental Economics and Policy, 11(2), 299–318. https://doi.org/10.1093/reep/rex012

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968

Ashenfelter, O. (2012). Predicting the quality and prices of Bordeaux wine. Journal of Wine Economics, 5(1), 40–52. https://doi.org/10.1017/s193143610000136x

Ashenfelter, O., Ashmore, D., & Lalonde, R. (2009). Bordeaux wine vintage quality and the weather. In S. Satchell (Ed.), Collectible investments for the high net worth investor (pp. 233–244). https://doi.org/10.1016/B978-0-12-374522-4.00012-3

Ashenfelter, O., & Storchmann, K. (2010). Measuring the economic effect of global warming on viticulture using auction, retail, and wholesale prices. Review of Industrial Organization, 37(1), 51–64. https://doi.org/10.1007/s11151-010-9256-6

Ashenfelter, O., & Storchmann, K. (2016). The economics of wine, weather, and climate change. Review of Environmental Economics and Policy, 10(1), 25–46. https://doi.org/10.1093/reep/rev018

Auffhammer, M. (2018). Quantifying economic damages from climate change. Journal of Economic Perspectives, 32(4), 33–52. https://doi.org/10.1257/jep.32.4.33

Blanc, E., & Reilly, J. (2017). Approaches to assessing climate change impacts on agriculture: An overview of the debate. Review of Environmental Economics and Policy, 11(2), 247–257. https://doi.org/10.1093/reep/rex011

Blanc, E., & Schlenker, W. (2017). The use of panel models in assessments of climate impacts on agriculture. Review of Environmental Economics and Policy, 11(2), 258–279. https://doi.org/10.1093/reep/rex016

Bock, A., Sparks, T. H., Estrella, N., & Menzel, A. (2013). Climate-induced changes in grapevine yield and must sugar content in Franconia (Germany) between 1805 and 2010. PloS One, 8(7), Article e69015. https://doi.org/10.1371/journal.pone.0069015

Burke, M., Dykema, J., Lobell, D. B., Miguel, E., & Satyanath, S. (2015). Incorporating climate uncertainty into estimates of climate change impacts. The Review of Economics and Statistics, 97(2), 461–471. https://doi.org/10.1162/REST_a_00478

Burke, M., & Emerick, K. (2016). Adaptation to climate change: Evidence from US agriculture. American Economic Journal: Economic Policy, 8(3), 106–140. https://doi.org/10.1257/pol.20130025

Butler, E. E., & Huybers, P. (2012). Adaptation of US maize to temperature variations. Nature Climate Change, 3(1), 68–72. https://doi.org/10.1038/nclimate1585

Camargo, S. J., & Hsiang, S. M. (2015). Tropical cyclones: From the influence of climate to their socioeconomic impacts. In M. Chavez, M. Ghil, & J. Urrutia-Fucugauchi (Eds.), Extreme events: Observations, modeling, and economics (pp. 303–342). https://doi.org/10.1002/9781119157052.ch18

Chavas, J.-P., Di Falco, S., Adinolfi, F., & Capitanio, F. (2019). Weather effects and their long-term impact on the distribution of agricultural yields: Evidence from Italy. European Review of Agricultural Economics, 46(1), 29–51. https://doi.org/10.1093/erae/jby019

Colson, A. R., & Cooke, R. M. (2018). Expert elicitation: Using the classical model to validate experts’ judgments. Review of Environmental Economics and Policy, 12(1), 113–132. https://doi.org/10.1093/reep/rex022

Cross, R., Plantinga, A. J., & Stavins, R. N. (2011). What is the value of terroir? American Economic Review, 101(3), 152–156. https://doi.org/10.1257/aer.101.3.152

Cross, R., Plantinga, A. J., & Stavins, R. N. (2017). Terroir in the New World: Hedonic estimation of vineyard sale prices in California. Journal of Wine Economics, 12(3), 282–301. https://doi.org/10.1017/jwe.2017.27

Daoud, A., & Dubhashi, D. (2023). Statistical modeling: The three cultures. Harvard Data Science Review, 5(1). https://doi.org/10.1162/99608f92.89f6fe66

Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climate-economy literature. Journal of Economic Literature, 52(3), 740–798. https://doi.org/10.1257/jel.52.3.740

Deschênes, O., & Greenstone, M. (2007). The economic impacts of climate change: Evidence from agricultural output and random fluctuations in weather. American Economic Review, 97(1), 354–385. https://doi.org/10.1257/aer.97.1.354

Fisher, A. C., Hanemann, W. M., Roberts, M. J., & Schlenker, W. (2012). The economic impacts of climate change: Evidence from agricultural output and random fluctuations in weather: Comment. American Economic Review, 102(7), 3749–3760. https://doi.org/10.1257/aer.102.7.3749

Geppert, C., da Cruz, M., Alma, A., Andretta, L., Anfora, G., Battaglia, D., Burgio, G., Caccavo, V., Chiesa, S. G., Cinquatti, F., Cocco, A., Costi, E., D’Isita, I., Duso, C., Garonna, A. P., Germinara, G. S., Bue, P. L., Lucchi, A., Maistrello, L., … Marini, L. (2024). Climate and landscape composition explain agronomic practices, pesticide use and grape yield in vineyards across Italy. Agricultural Systems, 215, Article 103853. https://doi.org/10.1016/j.agsy.2024.103853

Godden, P., Wilkes, E., & Johnson, D. (2015). Trends in the composition of Australian wine 1984–2014. Australian Journal of Grape and Wine Research, 21(S1), 741–753. https://doi.org/10.1111/ajgw.12195

Guilpart, N., Metay, A., & Gary, C. (2014). Grapevine bud fertility and number of berries per bunch are determined by water and nitrogen stress around flowering in the previous year. European Journal of Agronomy, 54, 9–20. https://doi.org/10.1016/j.eja.2013.11.002

Hsiang, S. (2016). Climate conometrics. Annual Review of Resource Economics, 8(1), 43–75. https://doi.org/10.1146/annurev-resource-100815-095343

Jarvis, C., Darbyshire, R., Goodwin, I., Barlow, E. W. R., & Eckard, R. (2019). Advancement of winegrape maturity continuing for winegrowing regions in Australia with variable evidence of compression of the harvest period. Australian Journal of Grape and Wine Research, 25(1), 101–108. https://doi.org/10.1111/ajgw.12373

Jones, G. V., Reid, R., & Vilks, A. (2012). Climate, grapes, and wine: Structure and suitability in a variable and changing climate. In P. Dougherty (Ed.), The geography of wine (pp. 109–133). Springer Netherlands. https://doi.org/10.1007/978-94-007-0464-0_7

Kolstad, C. D., & Moore, F. C. (2020). Estimating the economic impacts of climate change using weather observations. Review of Environmental Economics and Policy, 14(1), 1–24. https://doi.org/10.1093/reep/rez024

Maimaitiyiming, M., Sagan, V., Sidike, P., & Kwasniewski, M. (2019). Dual activation function-based extreme learning machine (ELM) for estimating grapevine berry yield and quality. Remote Sensing, 11(7), Article 740. https://doi.org/10.3390/rs11070740

Meloni, G., Anderson, K., Deconinck, K., & Swinnen, J. (2019). Wine regulations. Applied Economic Perspectives and Policy, 41(4), 620–649. https://doi.org/10.1093/aepp/ppz025

Mendelsohn, R., Nordhaus, W. D., & Shaw, D. (1994). The impact of global warming on agriculture: A Ricardian analysis. American Economic Review, 84(4), 753–771.

Mendelsohn, R. O., & Massetti, E. (2017). The use of cross-sectional analysis to measure climate impacts on agriculture: Theory and evidence. Review of Environmental Economics and Policy, 11(2), 280–298. https://doi.org/10.1093/reep/rex017

Molitor, D., & Keller, M. (2017). Yield of Müller-Thurgau and Riesling grapevines is altered by meteorological conditions in the current and previous growing seasons. Journal international des sciences de la vigne et du vin, 50(4), 245–258. https://doi.org/10.20870/oeno-one.2016.50.4.1071

Moore, F. C., & Lobell, D. B. (2014). Adaptation potential of European agriculture in response to climate change. Nature Climate Change, 4(7), 610–614. https://doi.org/10.1038/nclimate2228

Moore, F. C., & Lobell, D. B. (2015). The fingerprint of climate trends on European crop yields. Proceedings of the National Academy of Sciences, 112(9), 2670–2675. https://doi.org/10.1073/pnas.1409606112

Niklas, B. (2018). Impact of annual weather fluctuations on wine production in Germany. Journal of Wine Economics, 12(4), 436–445. https://doi.org/10.1017/jwe.2017.47

Oczkowski, E. (2016). The effect of weather on wine quality and prices: An Australian spatial analysis. Journal of Wine Economics, 11(1), 48–65. https://doi.org/10.1017/jwe.2015.14

Ortiz-Bobea, A. (2021). The empirical analysis of climate change impacts and adaptation in agriculture. In C. B. Barrett & D. R. Just (Eds.), Handbook of agricultural economics (Vol. 5, pp. 3981–4073). Elsevier. https://doi.org/https://doi.org/10.1016/bs.hesagr.2021.10.002

Ortiz-Bobea, A., Wang, H., Carrillo, C. M., & Ault, T. R. (2019). Unpacking the climatic drivers of US agricultural yields. Environmental Research Letters, 14(6), Article 64003. https://doi.org/10.1088/1748-9326/ab1e75

Pindyck, R. S. (2013). Climate change policy: What do the models tell us? Journal of Economic Literature, 51(3), 860–872. https://doi.org/10.1257/jel.51.3.860

Pindyck, R. S. (2017). The use and misuse of models for climate policy. Review of Environmental Economics and Policy, 11(1), 100–114. https://doi.org/10.1093/reep/rew012

Pindyck, R. S. (2019). The social cost of carbon revisited. Journal of Environmental Economics and Management, 94, 140–160. https://doi.org/10.1016/j.jeem.2019.02.003

Puga, G., Anderson, K., & Doko Tchatoka, F. (2022). Impact of growing season temperature on grape prices in Australia. Australian Journal of Grape and Wine Research, 28(4), 651–657. https://doi.org/10.1111/ajgw.12566

Puga, G., Anderson, K., Jones, G., Doko Tchatoka, F., & Umberger, W. (2022). A climatic classification of the world’s wine regions. OENO One, 56(2), 165–177. https://doi.org/10.20870/oeno-one.2022.56.2.4627

Puga, G., Anderson, K., & Tchatoka, F. D. (2023). The impact of climate change on grape yields: Evidence from Australia. OENO One, 57(2), 219–230. https://doi.org/10.20870/oeno-one.2023.57.2.7280

Quiroga, S., & Iglesias, A. (2009). A comparison of the climate risks of cereal, citrus, grapevine and olive production in Spain. Agricultural Systems, 101(1–2), 91–100. https://doi.org/10.1016/j.agsy.2009.03.006

Santos, J. A., Fraga, H., Malheiro, A. C., Moutinho-Pereira, J., Dinis, L.-T., Correia, C., Moriondo, M., Leolini, L., Dibari, C., Costafreda-Aumedes, S., Kartschall, T., Menz, C., Molitor, D., Junk, J., Beyer, M., & Schultz, H. R. (2020). A review of the potential climate change impacts and adaptation options for European viticulture. Applied Sciences, 10(9), Article 3092. https://doi.org/10.3390/app10093092

Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594–15598. https://doi.org/10.1073/pnas.0906865106

Schuurman, D., & Ker, A. (2025). Heterogeneity, climate change, and crop yield distributions: Solvency implications for publicly subsidized crop insurance programs. American Journal of Agricultural Economics, 107(1), 248–268. https://doi.org/10.1111/ajae.12446

Smith, S., & Alston, J. (2024). Climate, weather, and collective reputation: Implications for California’s wine prices and quality. AAWE Working Paper No. 283. https://wine-economics.org/wp-content/uploads/2024/08/AAWE_WP283.pdf

van Leeuwen, C., Schultz, H. R., De Cortazar-Atauri, I. G., Duchêne, E., Ollat, N., Pieri, P., Bois, B., Goutouly, J. P., Quénol, H., Touzard, J. M., Malheiro, A. C., Bavaresco, L., & Delrot, S. (2013). Why climate change will not dramatically decrease viticultural suitability in main wine-producing areas by 2050. Proceedings of the National Academy of Sciences, 110(33), E3051–E3052. https://doi.org/10.1073/pnas.1307927110

van Leeuwen, C., Sgubin, G., Bois, B., Ollat, N., Swingedouw, D., Zito, S., & Gambetta, G. A. (2024). Climate change impacts and adaptations of wine production. Nature Reviews. Earth & Environment, 5(4), 258–275. https://doi.org/10.1038/s43017-024-00521-5

Wooldridge, J. M., Wadud, I. K. M. M., Lye, J. N., & Joyeux, R. (2021). Introductory econometrics (2nd Asia-Pacific ed.). Cengage Learning Australia.

Yang, C., Menz, C., Fraga, H., Costafreda-Aumedes, S., Leolini, L., Ramos, M. C., Molitor, D., van Leeuwen, C., & Santos, J. A. (2022). Assessing the grapevine crop water stress indicator over the flowering-veraison phase and the potential yield lose rate in important European wine regions. Agricultural Water Management, 261, Article 107349. https://doi.org/10.1016/j.agwat.2021.107349

©2025 German Puga and Kym Anderson. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.