Integrating nature-based tourism and forestry in private lands under heterogeneous visitor preferences for forest attributes

Figures & data

Table 1. Attributes, attribute levels and variable names used in the choice experiment (status quo levels in italics).

Attributes	Attribute levels	Variable name
Alternative specific constant	n/a	ASC
Outdoor routes in private forests	Decrease: 80 km of routes	ROUT1
	No change: 100 km of routes
	Increase: 120 km of routes	ROUT3
Quality of landscape: traces of intensive forestry operations	No change: visible along 20% of routes
	Slightly improved: visible along 10% of routes	LAND2
	Clearly improved: not visible along routes	LAND3
Forest biodiversity: endangered species	Decrease: 10% of species extinct	BIO1
	No change: 200 endangered species
	Increase: populations increase by 10%	BIO3
Carbon sequestration by forests: carbon emissions eliminated	Decrease: emissions of 80,000 tourists	CARB1
	No change: emissions of 100,000 tourists
	Increase: emissions of 120,000 tourists	CARB3
Payment for environmental management: €/visitor/week	No payment, 5€, 10€, 20€, 30€ and 50€	PAY

Table 2. Principal components based on attitudinal statements related to the management of forest resources and landscape and environmental amenities (Varimax rotation with Kaiser normalization. Loadings of 0.50 or above in boldface).

	I	II	III	IV
Timber production should be limited for enhanced biodiversity	0.808	0.074	−0.007	0.135
Timber production should be limited for a more pleasant landscape	0.748	−0.036	0.013	0.163
Timber production should be limited for preventing climate change	0.664	0.145	−0.063	0.033
Forests are already sufficiently protected so there is no need for additional protection	−0.607	−0.044	0.305	−0.013
The forests of the area should mainly be managed for maximizing timber production	−0.594	0.113	0.276	−0.031
Biodiversity should be preserved	0.583	0.217	−0.010	−0.140
Off-road vehicles disturb nature and other people in nature	0.409	0.325	0.193	−0.193
People can slow down climate change by changing their behaviour	0.409	0.374	0.024	−0.224
Tourists should pay their share for preserving the landscape	0.034	0.793	0.056	−0.013
I would be prepared to pay my own share into the Ruka-Kuusamo Environmental Management Fund	0.101	0.768	−0.069	0.206
Forest-owners’ participation in landscape management should be voluntary	−0.140	−0.231	0.780	0.053
Private forest owners should be compensated for the costs of landscape management and lost income	−0.065	0.359	0.650	0.018
More hiking and skiing routes should be opened in the forests of the area	−0.121	0.098	0.042	0.728
Privately owned forests should be managed primarily with the interests of tourism in mind	0.282	−0.016	0.014	0.694
Eigenvalue (rotation sum)	3.177	1.745	1.253	1.211
Cumulative variance explained, %	22.69	35.16	44.10	52.76

Note: Interpretation of principal components:

I Environmentalism,

II the user pays principle,

III forest owner's view, and

IV tourism business orientation.

Table 3. Visitor groups based on attitudinal principal components (K-means clustering).

		Mean of original variable scores
Visitor group	n	I	II	III	IV
1. Responsible recreationalists	460	3.63	3.82	3.08	3.16
2. Forest-owner minded	341	3.49	3.29	4.27	2.83
3. Everyman's rights enthusiasts	283	3.35	2.13	2.99	2.78
F ratio		41.24	676.15	544.12	28.98
p Value		0.000	0.000	0.000	0.000

Note: Interpretation of principal components:

I Environmentalism,

II the user pays principle,

III forest owner's view, and

IV tourism business orientation.

Table 5. Estimated latent class model with marginal WTP effects for significant variables.

	Class 1: Common outdoors activists			Class 2: Environmentalists			Class 3: Well-behaved majority
	Coefficient	Std. error	WTP	Coefficient	Std. error	WTP	Coefficient	Std. error	WTP
ASC	*−0.6486	0.381	−3.93	0.0988	0.228		***−1.5199	0.125	−76.15
ROUT1	−0.0896	0.441		***−1.4933	0.269	−18.27	***−0.3305	0.097	−16.56
ROUT3	0.3803	0.424		0.0091	0.271		*−0.1950	0.108	−9.77
LAND2	−0.5257	0.360		***0.9720	0.290	11.89	**0.2263	0.101	11.34
LAND3	***2.4060	0.504	14.58	0.1436	0.300		***0.3245	0.075	16.26
BIO1	***−2.7852	0.453	−16.88	***−2.0375	0.319	−24.93	***−1.0065	0.098	−50.43
BIO3	−0.2876	0.315		***0.5649	0.180	6.91	***0.3391	0.080	16.99
CARB1	−0.0999	0.340		***−0.7965	0.268	−9.75	0.0708	0.103
CARB3	***−1.4398	0.522	−8.72	**0.6363	0.263	7.79	0.0110	0.102
PAY	***−0.16 504	0.032		***−0.08 172	0.010		***−0.01 996	0.002
Class probabilities		0.143			0.364			0.494
R squared		0.3048
Log-likelihood		−4336.73
AIC/N		1.529
BIC/N		1.566
N		5715

***significant at 0.01 level.

**significant at 0.05 level.

*significant at 0.10 level.

Table 6. Compensating variations (CV) for management scenarios (€/visitor/week).

	Latent class model (probability)			Conditional logit
Scenario	Class 1 (0.143)	Class 2 (0.364)	Class 3 (0.494)
1. Recreation oriented	14.58	0.00	6.49	13.03
2. Multiple-use forestry	14.58	6.91	33.25	23.29
3. Environmentally friendly	−8.72	14.7	16.99	10.26

Table 4. Estimated conditional logit model with marginal WTP effects for significant variables.

	Coefficient	Std. error	WTP
ASC	***−0.5148	0.071	−18.74
ROUT1	***−0.3481	0.061	−12.67
ROUT3	−0.0868	0.068
LAND2	***0.2655	0.061	9.66
LAND3	***0.3580	0.055	13.03
BIO1	***−1.0922	0.066	−39.75
BIO3	***0.2819	0.049	10.26
CARB1	−0.0956	0.062
CARB3	−0.0480	0.062
PAY	***−0.02 748	0.001
R squared	0.0920
Log-likelihood	−5663.8028
AIC/N	1.986
BIC/N	1.997
N	5715

***significant at 0.01 level.

**significant at 0.05 level.

*significant at 0.10 level.

Table 7. Latent class model with attitudinal variables (class membership parameters for class 3 fixed to identify other coefficients).

	Class 1: Common outdoors activists			Class 2: Well-behaved majority			Class 3: Environmentalists
	Coefficient	Std. error	WTP	Coefficient	Std. error	WTP	Coefficient	Std. error	WTP
ASC	−0.0489	0.286		***−3.0930	0.513	−215.99	***−0.6763	0.138	−18.64
ROUT1	*** −1.3997	0.341	−9.57	***−0.6312	0.197	−44.08	***−0.3124	0.113	−8.61
ROUT3	−0.3313	0.340		***−0.6799	0.235	−47.48	0.1636	0.127
LAND2	0.0587	0.284		0.2908	0.215		***0.3099	0.114	8.54
LAND3	***0.8048	0.257	5.50	*0.2245	0.117	15.68	***0.5175	0.105	14.26
BIO1	***−2.1860	0.392	−14.94	***−0.8113	0.177	−56.66	***−1.4002	0.162	−38.60
BIO3	0.2094	0.202		***0.5260	0.165	36.73	**0.2001	0.093	5.51
CARB1	−0.2920	0.285		0.1722	0.201		−0.0911	0.133
CARB3	0.1936	0.344		0.1168	0.205		−0.0228	0.117
PAY	***−0.1463	0.026		***−0.0143	0.003		***−0.0363	0.004
Class membership parameters
Intercept	***3.4462	1.055		***−4.6462	1.283
SUM1P	** −0.6099	0.278		**0.7202	0.336
SUM2P	*** −0.4204	0.137		***0.5686	0.165
SUM3P	0.0054	0.021		0.0032	0.007
SUM4P	**−0.2868	0.144		−0.1613	0.156
Class probabilities		0.174			0.418			0.408
R squared		0.3132
Log-likelihood		−4284.02
AIC/N		1.513
BIC/N		1.560
N		5715

***significant at 0.01 level.

**significant at 0.05 level.

*significant at 0.10 level.