Descriptive statistics and results
The reported number of visits per year varies substantially among the three countries (Table 3). It is largest in Finland, where the mean number of visits is 12.9, while it only amounts to 6.3 in Germany and 5.4 in Latvia. Respondents from Finland are thus more frequent visitors of their most often visited Baltic Sea site compared to respondents from Germany and Latvia. Moving from reported to hypothetical visits, it is noticeable that the average number of visits declines for the changed environmental conditions for Finland and Latvia while it stays nearly the same for Germany.
The mean Euclidean distance between residence and most often visited recreation site, mean travel time, and mean travel costs per country are interrelated and influenced by the size and shape of the three countries. Respondents from Germany travel furthest and longest to their favorite Baltic Sea sites, with distances being almost three time larger compared to Latvia. Respondents from Finland face intermediate levels of distance and travel time to travel to their most often visited Baltic Sea site. This carries over to varying levels of travel costs. These findings reflect the sizes of the different countries, as Latvia is much smaller than Germany and Finland. But it also reflects the fact that Finns live on average closer to the Baltic Sea than Germans even though the countries have a similar size. Moreover, these findings also reflect the fact that the German sample contains a much larger share of tourists, i.e., respondents who live more than 30 km away from the Baltic Sea and stayed more than 12 h at the site (56% tourists in Germany compared to 17% in Finland and Latvia). Related to this, looking at the map in Fig. 1, it seems that respondents from Germany more often chose a Baltic Sea site outside of their own country than respondents from the other countries, which also explains larger distances, travel times, and travel costs.
Regarding other socio-economic characteristics, respondents from Germany are on average four to 5 years older than respondents from Finland and Latvia. The share of respondents with high school education as their highest educational level is similar between countries. The share of university educated respondents, however, varies substantially between countries (44% in Finland, 34% in Germany, and 23% in Latvia). Income adjusted for PPP is highest in the German sample and lowest in the Latvian sample.
The respondents’ perceptions of the average environmental conditions at their most often visited Baltic Sea site differ among the countries and quality attributes (Fig. 3). Overall, environmental quality is seemingly perceived to be better in Germany than in Finland and Latvia. For the water clarity attribute, for example, almost 80% of the respondents from Germany perceive the water to be clear or somewhat clear, while this share only amounts to 66% in Latvia and 38% in Finland. Similar patterns hold for the other environmental attributes (blue-green algal blooms, algae onshore, and bird species diversity) and for the attribute facilities at the site. Almost 70% of the respondents from Germany describe their Baltic Sea sites as being equipped with many facilities. This only holds for 39% of the favorite sites in Finland and 27% of the favorite sites in Latvia.
Our findings correspond to previous studies, where German respondents have been found to be the least concerned of the environmental status of the Baltic Sea (Ahtiainen et al. 2013, 2014), and have the most positive perceptions of the local environmental status (i.e., the German marine waters of the Baltic Sea) of all the coastal countries (Ahtiainen et al. 2013; Czajkowski et al. 2015). There are no evident differences in actual environmental quality in the sub-basins adjacent to Germany compared to those adjacent to Finland and Latvia, at least on the sub-basin level (HELCOM 2018). Thus, the differences in perceptions are likely rooted in other factors we can only hypothesize about. One reason could be that the water quality at substitute sites is much lower in Germany than in Finland. Another reason could be that German respondents, who live on average much further away from the Baltic Sea than Finnish respondents, have a lower attachment to the Baltic Sea and are thus less concerned about its environmental state. However, as the spatial aggregation of the HELCOM data is quite coarse, it is also possible that there are more pronounced differences in environmental quality closer to the shore where they are experienced by the respondents.
The respondents used all categories when rating the perceived environmental quality at their most often visited sites, but the more “extreme” categories were chosen less often than the middle categories. For example, respondents from Germany chose the best category in 30% of the cases for the attributes water clarity and blue-green algal blooms, and in 20% of the cases for algae onshore. Also, respondents from Latvia chose the best category for the attribute blue-green algal blooms in almost 30% of the cases and in more than 10% of the cases for the attributes water clarity and algae onshore. The worst categories were chosen less often in all countries.
Table 4 shows the share of respondents who faced an improvement or a deterioration in the hypothetical CB scenarios compared to their perceived SQ situation. The reported share is the average share for the three hypothetical situations separated by country. The share of respondents who did neither face an improvement nor a deterioration did either not face a change in the CB scenarios or did not indicate their perceptions of the respective quality attribute for the SQ. This is valuable information to get an overview for which attributes and in which countries respondents were more faced with improving or deteriorating situations.
The quality levels presented to the respondents in the hypothetical situations were randomized. Consequently, the probability to face a quality improvement in a CB scenario would increase when the respondent observed low quality levels for the actually visited site (SQ). Likewise, the probability to face quality deteriorations in a hypothetical CB scenario would increase when high quality levels were reported for the SQ at the actually visited site. This is reflected in Table 4. For the attribute water clarity, for example, respondents from Germany faced quality deteriorations in 48% of all situations averaged over all CB scenarios, but improvements in only 23% of all situations. In Finland and Latvia, this relation was more balanced. The same pattern can be observed for the other attributes. This reflects the finding that respondents from Germany, overall, perceived environmental conditions in the SQ to be better than respondents from the other countries.
Since respondents in the three countries differ strongly in their perceptions of the SQ, also the reference point differs (see Table 5). For example, median perceptions of water clarity are lower for Finland than for Germany and Latvia. For blue-green algal blooms and bird species diversity, the median perception is equal across all three countries. For the attributes algae onshore and facilities, in contrast, median perceptions are better for Germany than for Finland and Latvia. Taking also mean perceptions into account, environmental quality is seemingly perceived to be better in Germany than in Finland and Latvia (compare also Fig. 3). It can thus be expected that the estimation results will differ among countries regarding whether improvements or deteriorations are considered. In particular, the perceived environmental quality at the most often visited site is likely to influence the respondents’ preferences for environmental conditions, and thus the impact of environmental changes on individual recreational behavior. Thus, it is important to allow for differing reference points across the countries, and to discuss the results relative to the reference condition.
In Table 6, we present the estimation results for the linear asymmetric negative binomial random-effects model estimated separately for each country. The test statistics of a likelihood ratio test comparing a model with a beta-distributed overdispersion parameter to a constant dispersion model indicate that the random-effects panel model fits the data better than the pooled model for all countries.
In the linear asymmetric model, travel costs (TC time) have a negative and significant influence on the number of visits in all countries, as expected. Even though quality changes are not always significant, the estimated coefficients have the expected signs for the attributes water clarity, blue-green algal blooms, algae onshore, and bird species diversity. For all the environmental attributes, improvements have a positive and deteriorations a negative effect. Overall, there seem to be asymmetries in the effects on the number of trips relative to the reference point, as the coefficients for improvements and deteriorations differ in their absolute value and sometimes significance. This is confirmed by Wald tests to determine whether the differences in the absolute values of the parameters for improvements and deteriorations are significantly different from zero. The tests indicate significant differences at the 5% level for all cases, except for the bird diversity attribute for Latvia.
Note that the estimated coefficients are half-elasticities, implying that they represent a percentage change in the number of visits induced by a one unit change in the respective explanatory variable. Taking the attribute water clarity as an example, this implies that a one level increase in water clarity would ceteris paribus increase the expected number of visits by 9% for the case of Latvian respondents. A one level decrease in water clarity, in contrast, would ceteris paribus decrease the expected number of visits by 27% for Latvian respondents.
The results do not paint a clear picture of whether deteriorations or improvements result in larger relative effects on the number of trips, as these differ by attribute and country. For the attribute water clarity, for example, both improvements and deteriorations have a significant effect on the number of visits for the case of Finland and Latvia, while for Germany only deteriorations have a significant effect. For Finland, improvements in water clarity have a stronger relative impact on the number of visits than deteriorations. The opposite result can be observed for Latvia, where deteriorations in water clarity have a stronger relative impact on the number of visits than improvements. The reason for this pattern might be that respondents in Germany and Latvia perceive water clarity as being rather clear. As described above, 80% of the German respondents and a little less than 70% of the Latvian respondents perceive the water at their most often visited Baltic Sea site to be clear or rather clear. This share only amounts to 35% in Finland. Consequently, respondents from Finland would greatly appreciate improvements in water clarity but would also react to further deteriorations. Respondents from Germany and Latvia, in contrast, would not benefit from further improvements but would strongly react to deteriorations, which would constitute a greater “loss” for them.
The effect of the attribute facilities merits closer attention. For Germany and Latvia, an increase in the number of facilities does not have a significant effect but the effect of a decrease in the number of facilities is significantly negative. Respondents from Germany and Latvia would thus be significantly negatively affected by decreasing facility levels at their most often visited sites. For Finland, however, both increasing and decreasing the number of facilities would have a significantly negative impact on the number of visits. Consequently, respondents from Finland seem to prefer the current equipment of the recreation sites they have selected, and would visit less often given changes in any direction.
The results of the non-linear asymmetric model are, in many respects, similar to the linear model (Table 7). The coefficient for travel costs is again negatively significant and of the same magnitude as in the linear model. In general, improvements have a positive and deteriorations a negative impact on the number of visits, but there are notable differences across countries. Regarding the environmental attributes, changes in water clarity are significant in explaining the number of visits in all countries, with deteriorations leading to larger relative impacts in Germany and Latvia and improvements leading to larger relative impacts in Finland. Regarding the other environmental quality attributes, blue-green algae and algae onshore explain the number of visits at least to some extent. The bird diversity attribute is insignificant in Germany and Latvia and only weakly significant in Finland. Changes in the number of facilities in any direction lead to reductions in visits for Finland, while for the case of Germany only decreases in facilities have a negative effect on the number of visits.
We used Wald tests to assess whether there were non-linearities in the relative effects of single attributes on the number of visits for single attributes, separately for improvements and deteriorations. The results indicated significant non-linear effects only for deteriorations in blue-green algal blooms and increases in facilities both for Finland and Latvia. No significant non-linear effects were found for Germany.Footnote 6 In addition, we performed a likelihood ratio (LR) test to test whether the linear or the non-linear asymmetric model would fit the data better. For Germany and Latvia, no significant difference could be detected (p = 0.9705 and p = 0.1596, respectively), such that a linear asymmetric model should suffice to fit the data. This is supported by the lower values of AIC and BIC for the linear specification. For Finland, in contrast, the likelihood ratio test indicated that the non-linear model fits the data significantly better (p = 0.0048), also reflected by the lower AIC and BIC values.
Scenario Analysis for Changes in Annual Consumer Surplus
In this section, we present estimated changes in the expected number of visits and individual CS per year for selected scenarios (Table 8). We first predicted the expected number of visits under the assumption that all environmental quality attributes (water clarity, blue-green algae, algae onshore and bird diversity) obtain their best and worst levels and facilities are at their SQ level (Scenario I and II). Second, we assumed that environmental quality attributes would be at their SQ levels, but the number of facilities would change to the highest (many) or lowest (none) level (Scenario III and IV). In all cases, we consider changes in relation to the respondents’ perceived SQ (see also Table 5). Additional scenarios, as well as welfare estimates for individual attributes are provided in Table S6 in the supplementary online material.
When interpreting the results, it is important to keep in mind that there are differences in the SQ levels of the attributes across countries. Respondents from Germany perceive the environmental conditions at their most often visited site to be better than the respondents from Finland and Latvia (Table 5). Thus the average improvement for German respondents (averaged over all four environmental attributes, excluding facilities) is approximately one level (1.1) when moving from the perceived SQ to the best environmental scenario. The average improvement for Finnish and Latvian respondents, in contrast, is 1.5 and 1.4 levels, respectively. Likewise, the deterioration is on average larger for respondents from Germany (1.9 levels) when moving from the perceived SQ to the worst environmental scenario, compared to 1.5 levels for Finland and 1.6 levels for Latvia. Thus, changes from the SQ level to the best and the worst environmental scenarios are similar-sized for Finland and Latvia (approximately 1.5 levels), while for Germany the change from the SQ to the worst environmental scenario is larger (1.9) than the change to the best environmental scenario (1.1). For facilities, the changes from the SQ to the highest level (many facilities) are 0.7, 0.3 and 0.9 and to the lowest level (no facilities) are 1.3, 1.7 and 1.1 for Finland, Germany and Latvia, respectively. Thus, the extent of the change to the lowest level is larger than the change to the highest level for Finland and Germany, while for Latvia the changes are of relatively equal size.
In Table 8, we compare the predicted number of visits under current conditions and the predicted number of visits under changed conditions for the four scenarios described above. Changes in individual CS per year are calculated based on Eq. (5).Footnote 7 For changes in the four environmental quality attributes (scenarios I and II), it can be observed that for Finland and Latvia, the expected number of visits reacts more strongly to environmental changes in the worst environmental scenario compared to the best environmental scenario. In the best environmental scenario, the expected number of visits to the most often visited Baltic Sea site would increase by 2.8 visits per year for Finland and by 0.6 visits per year for Latvia, while in the worst environmental scenario for these two countries, the expected number of visits would reduce by 3.5 and 2.3 visits per year, respectively. For the case of Germany, the expected number of visits would increase by 1.9 per year in the best environmental scenario and decrease by 1.8 visits per year in the worst scenario, although the average change from the SQ to the worst level is notably larger than the change to the best level. Overall, changes in facilities result in smaller effects on the number of visits (scenarios III and IV). Moving from the SQ level to either direction reduces the expected number of visits for Finnish and Latvian respondents, while German respondents would make more trips if the number of facilities increased and less if it decreased.
The changes in individual consumer surplus (CS) reflect these results. For the environmental scenarios (I and II), both absolute and relative changes in average annual CS per visitor are larger in the worst scenario than in the best scenario for the case of Finland and Latvia. In addition, this effect seemingly goes beyond asymmetries simply driven by the size of perceived changes when moving from the SQ to a policy scenario. In particular, for the case of Finland and Latvia, the extent of perceived improvements from the SQ to the best environmental scenario is quite similar to the extent of perceived deteriorations to the worst scenario. Still, changes in annual CS are notably larger in the worst environmental scenario than in the best scenario. For Germany, in contrast, the extent of perceived improvements in the best environmental scenario is much smaller than the extent of perceived deteriorations in the worst scenario. Still, relative changes in annual visitation numbers and CS are similar in both scenarios. For changes in facilities (scenarios III and IV), Finnish and Latvian respondents experience welfare losses from changes to a higher or lower level of facilities compared to the SQ, with a higher welfare effect resulting from a decrease in the number of facilities. For German respondents, having more facilities results in increased CS and having less in decreased CS, and the size of the effect is again independent of the direction of the change.
In all countries, the welfare effects of changes in the four environmental attributes (water clarity, blue-green algal blooms, algae onshore and bird diversity) are larger than the effect of changes in facilities. This is especially true for Germany, where changes in environmental attributes to the best or worst levels result in more than four times larger changes in CS than changes in facilities. Considering the effects of individual attributes (Table S6 in supplementary online material), deteriorations with respect to blue-green algal blooms result in the largest welfare effects for the case of Finland. For Germany and Latvia, in contrast, a decrease in water clarity causes the largest impacts on CS.
Aggregate Consumer Surplus
Aggregate CS estimates related to changes in the marine environment are relevant for policy purposes, e.g., when estimating the economic benefits of achieving the objectives of European and Baltic Sea marine policies. To date, most policy-relevant benefit estimates are based on stated preference methods (Nieminen et al. 2019; Norton and Hynes 2018; Kosenius 2010; Ahtiainen et al. 2014). Results from TC and CB studies complement the existing information. While acknowledging that aggregation entails significant uncertainties as our sample is limited to those who have visited the Baltic Sea in the last 3 years, we provide aggregate welfare estimates for the three countries using the shares of non-users and assigning them zero values (Table 9). These aggregate estimates reflect the benefits and losses of those who currently visit the Baltic Sea and do not account for the possible benefits of people who are currently non-visitors but might start visiting the Baltic Sea if conditions improved. Thus, the estimates should be taken as indicative of the total value of changes in the Baltic Sea environmental conditions for Finland, Germany, and Latvia.
The aggregate CS estimates underline that the recreational use of the Baltic Sea yields considerable welfare for the citizens of the riparian countries based to a large extent on good environmental conditions. The aggregate CS estimates reflect the population size of the country but also take into account the share of non-users in the sample. Given the large population size, the current total annual CS for Germany amounts to 7276.6 billion EUR and is five times as large as the Finnish equivalent (1531.6 billion EUR), even though the share of non-users is pronouncedly larger in Germany than in Finland. Lower population size combined with lower individual CS per visit result in much lower but still considerable current total annual CS for Latvia (47.8 billion EUR). Reflecting the results presented in Sect. 4.3, the largest absolute and relative changes in total CS occur when environmental conditions obtain their best or worst levels (scenarios I and II), particularly for the case of Germany. The smallest absolute and relative effects on total CS can be observed when facilities change while environmental conditions remain at their SQ levels (scenarios III and IV), in particular for the case of Latvia.
Robustness Checks and Comparison to Former Findings
The econometric approach taken in this study did not explicitly account for the fact that respondents could also visit other sites as potential substitutes for their favorite Baltic Sea site. Rather, this was addressed in the framing of the CB questions by stating explicitly that environmental quality would only change at the most often visited site and the surrounding area and not in the remaining Baltic Sea. We, therefore, ensure that conditions in the remaining Baltic Sea are kept constant and avoid having to make unrealistic assumptions when calculating the welfare estimates. Moreover, we asked respondents not only how often they visited their favorite Baltic Sea site but also the Baltic Sea in general. The number of respondents who report the same number of visits to their most often visited site and to the Baltic Sea in general is relatively low for Finland (425 respondents, i.e., 42% of the total sample) and Latvia [305 respondents (58%)] but considerable for Germany [403 respondents (70%)]. We have included a linear asymmetric regression for these subsamples in the supplementary online material (Table S1) to check the robustness of our main results. The results show a similar pattern to the results for the full sample even though a few effects are no longer significant in the reduced sample. The magnitudes and signs of the coefficients, however, seem to be quite robust to reducing the sample.
We carried out two further robustness checks to look at the effects of weighting travel costs according to the purpose of the trip. Table S2 in the supplementary online material shows a linear asymmetric regression in which we only include the subsample of respondents who have visited no other than their single favorite site over the last 3 years. Table S3 shows a linear asymmetric regression for the full sample but with an alternative weighting scheme, putting substantially more weight on those visits for which recreation is the only or main purpose of the trip and substantially less weight on those visits for which recreation was only a minor purpose. The travel cost weights assigned in this alternative specification are 100% (only purpose), 75%, 5%, 3%, and 1% (only a small purpose). The results for the reduced sample differ slightly to our main regressions which can be expected due to the substantially reduced sample sizes in the three countries. However, the results of the main regressions are very robust regarding a change in the weighting scheme.
Comparing our results to former studies is difficult since no studies using the same methods for valuing Baltic Sea recreational benefits are available. Czajkowski et al. (2015) used the TC approach to calculate the recreation benefits provided by the Baltic Sea for all riparian countries. The CS per visit is reported to be 80.7 Euros/visit for Finland, 77.6 Euros/visit for Germany, and 28.3 Euros/visit for Latvia, which is much lower than our estimates (366 Euros/visit for the Finnish sample, 419 Euros/visit for the German sample, and 65 Euros/visit for the Latvian sample). However, Czajkowski et al. (2015) use a zero-inflated negative binomial model including users and non-users of the Baltic Sea coast in the estimation sample while we only include those respondents who had visited the Baltic Sea coast at least once over the last 3 years. This is reflected by the fact that the estimation samples in Czajkowski et al. (2015) show, on average, much lower numbers of reported trips, larger distances, and larger travel costs, both with and without opportunity costs of time.
Still, we carried out a further robustness check and fitted a symmetric travel cost model to our user samples in the three countries. For this regression, we neglected the information from the CB scenarios and only used the information on the actual trip. Accordingly, no panel model was necessary and a simple negative binomial model was used. In addition, we did not distinguish between improvements and deteriorations. This led to the following CS estimates: 93 Euros/visit for the Finnish sample, 196 Euros/visit for the German sample, and 37 Euros/visit for the Latvian sample (for a full set of estimation results, see Table S5 in the supplementary online material). These are still larger values than the estimates reported by Czajkowski et al. (2015). However, the authors also report that, on average, 55% of their respondents had taken no trips to the Baltic Sea. In our case, the share of non-users in the original sample, which we did not include in the estimations, amounts to 24% for Finland, 51% for Germany, and 21% for Latvia.
Lankia et al. (2019) used the CB method to value changes in water quality as we do but there are several differences to our study. Firstly, they focus on Finland only. Secondly, they focus on swimming trips and do not include other forms of recreational activities. Thirdly, they include all water bodies including freshwater lakes and rivers, which are scattered throughout the country and cover 10% of Finland’s surface area. This results in a much larger number of reported trips, smaller distances, and smaller travel costs, both with and without opportunity costs of time. Consequently, the CS estimates they report are substantially smaller than ours, amounting to 16 Euros/visit for respondents that go by car and 7 Euros/visit for respondents who walk or go by bike.