6. Discussion and conclusions

than the logit model as measured by pseudo R<sup>2</sup>

Table 2. Regression results of Model 1 with different estimators, N ¼ 59.

p < 0.1; <sup>a</sup>

The results for the two estimators are similar when replacing Loglength and Logmsec with VIX,

Constant �3.891 0.331 �2.396 0.215 Logelprod 0.841\*\*\* 0.000 0.496\*\*\* 0.000 Loglength �0.777\* 0.061 �0.443\*\* 0.041 Logmsec �1.334\*\*\* 0.001 �0.785\*\*\* 0.000 Mindisch 1.826\* 0.086 1.062\* 0.053

Variable name Logit Probit

Model significance<sup>a</sup> Pseudo <sup>R</sup><sup>2</sup> <sup>p</sup> <sup>¼</sup> 0.000 <sup>p</sup> <sup>¼</sup> 0.000

Predicted Cost ¼ 1/observed Cost ¼ 1 33/38 33/38 AIC, BIC 54.177, 64.565 53.851, 64.238

Variable name Logit Probit

Model significance<sup>a</sup> Pseudo <sup>R</sup><sup>2</sup> <sup>p</sup> <sup>¼</sup> 0.004 <sup>p</sup> <sup>¼</sup> 0.004

AIC, BIC 23.339, 27.322 23.338, 27.321

Predicted yes/observed yes 10/13 10/13

Chi-square(3).

p < 0.1; <sup>a</sup>

Table 3. Regression results of Model 2 with different estimators, N ¼ 20.

Chi-square(4).

The statistical performance of Model 2 as measured by the significance of explanatory variables,

explained by the lower number of observations. A common result for Model 1 and Model 2 was the positive and significant effect of Logelprod. Although Mindisch has the expected negative sign in Model 2, it was not significant. The estimate of VIX has an unexpected negative sign. Since VIX

Constant �9.811 0.013 �5.825 0.004 Logelprod 0.731 0.013 0.430 0.002 VIX �2.644 0.655 �1.597 0.496 Mindisch 0.750 0.754 0.464 0.659

0.408 0.407

Bayesian Information Criterion (BIC) tests.

overall model significance pseudo R<sup>2</sup>

see Table 3.

Notes: \*\*\*p < 0.01, \*\*p < 0.05, \*

126 Selected Studies in Biodiversity

Notes: \*\*\*p < 0.01, \*\*p < 0.05, \*

, Aikaike Information Criterion (AIC), and

, AIC, and BIC was lower than for Model 1, which may be

Coeff. Prob. Coeff. Prob.

Coeff. Prob. Coeff. Prob.

0.440 0.444

The purpose of this study was to determine if restoration of biodiversity in dry channels at hydropower plants in Sweden can be costly for the plants and how the probability of a cost is affected by the size of the plant, site-specific factors in the dry channels, and ecological status in downstream regions of the river. The measure considered for restoration is the existence of a program for minimum releases of water from the reservoirs to the dry channel, and the cost is defined as a decrease in electricity production. The study rests on data from a survey of the largest hydropower plants in Sweden, which resulted in data for 76 plants with dry channels.

According to the responses in the survey, 58% of the plants with a program for minimum water discharges report a cost. The reasons for not reporting such a loss can be that it is considered as negligible or that the respondent has insufficient information. We cannot distinguish between these reasons, but it can be argued that impacts of releases of water from the reservoirs to the dry channels on electricity production would be shown in the continuous monitoring of electricity production. Nevertheless, we should be careful in interpreting the lack of reporting of a loss as the nonexistence of decreases in electricity production from programs on minimum discharges to dry channels.

The main results from our analysis of the different variables in explaining the probability of a reported cost are that the existence of a program for minimum water releases and a larger size of the plant as measured by kWh electricity production increase the probability. On the other hand, site characteristics as measured by the flow of natural water into the dry channel and length of the dry channels reduce the probability. These results point out potential cost savings for improving biodiversity in dry channels at hydropower plants by targeting water releases from reservoirs.

A cost-effective restoration policy requires that restoration measures are directed toward locations with high biodiversity impacts (e.g., [17]). Admittedly, due to lack of data on the impact of restoration measures on biodiversity, our results can give only partial guidance on the costeffective restoration of biodiversity loss by means of water releases from reservoirs. Despite this limitation, the results can be useful when considering that current Swedish policy is to a large extent based on uniform regulations for all hydropower plants, such a maximum loss of 2.3% in the annual production of electricity [18]. Our results show that the probability of costs in terms of losses in electricity production is low for relatively small-sized plants, and where the natural flow of waters to the dry channels is high and the length of the channels is large. Thus, a comparison of costs and effects of current uniform policy with a policy targeting restoration measures toward plant sites with these characteristics can be of interest for economic analysis.
