CC BY
5Journal of Siberian Federal University. Humanities & Social Sciences 2022 15(7): 955-964
DOI: 10.17516/1997-1370-0900 yflK 330.15
Is Timber Investment Actually the Driver of Logging Growth and Human Welfare?
Ekaterina D. Ivantsova*
Siberian Federal University Krasnoyarsk, Russian Federation
Received 06.03.2022, received in revised form 20.03.2022, accepted 05.04.2022
Abstract. Investment in the forest sector is one of the critical issues for the development of the forest industry in Russia. To form an effective sectoral investment policy, it is necessary to accurately understand the causes and consequences of key processes in the industry. In particular, it is important to establish how investments are reflected in timber harvesting, and whether there is a connection between these processes and the well-being of the population. The study is aimed at identifying causal relationships between investment in the forestry sector, logging, and the well-being of the population by identifying Granger causality in vector autoregressive models. After testing for stationarity of the series, we evaluated a five-equation vector autoregression model and performed zero-constraint F-tests to identify causality. The results show that there is a bi-directional positive causality between logging volume and investment in forestry and pulp and paper production. The causality between investment in forestry and investment in pulp and paper production is also bi-directional and positive. In addition, one Granger cause has been identified for the growth of GRP, which is investments in wood processing and the production of wood products. Thus, the results suggest that investments in forestry and the forestry industry are important determinants of logging volumes in the Russian forest sector, and vice versa. At the same time, for the welfare of the population, the causality was revealed only on the part of investments in wood processing, which is also a practically important result in the context of stimulating investment policy in the forestry sector.
Keywords: investment policy, forest industry, timber investment, Granger causality.
The study was funded by the Russian Science Foundation (project no. 19-18-00145, https:// rscf.ru/en/project/19-18-00145/).
Research area: economics.
Citation: Ivantsova, E. D.(2022). Is timber investment actually the driver of logging growth and human welfare? J. Sib. Fed. Univ. Humanit. soc. sci., 15(7), 955-964. DOI: 10.17516/1997-1370-0900
© Siberian Federal University. All rights reserved
* Corresponding author E-mail address: eivantsova@sfu-kras.ru
Действительно ли инвестиции в лесной комплекс являются драйвером роста лесозаготовок и благосостояния людей?
Е. Д. Иванцова
Сибирский федеральный университет Российская Федерация, Красноярск
Аннотация. Инвестиции в лесной сектор являются одной из критически важных проблем развития лесной промышленности в России. Чтобы сформировать эффективную отраслевую инвестиционную политику, необходимо точно понимать причины и следствия ключевых процессов в отрасли. В частности, важно установить, как отражаются инвестиции в заготовке древесины и существует ли связь этих процессов с благосостоянием населения. Исследование направлено на выявление причинно-следственных связей между инвестициями в лесном секторе, лесозаготовкой и благосостоянием населения с помощью выявления причинности по Гренджеру в моделях векторной авторегрессии. После проведения теста на стационарность рядов мы оценили модель векторной авторегрессии из пяти уравнений и провели F-тесты нулевых ограничений для выявления причинности. Полученные результаты показывают, что есть двусторонняя положительная причинность между объемом лесозаготовок и инвестициями в лесное хозяйство и в производство целлюлозно-бумажной продукции. Причинность между инвестициями в лесное хозяйство и инвестициями в производство целлюлозно-бумажной продукции также двусторонняя и положительная. Кроме того, для роста ВРП выявлена одна причина по Гренджеру -инвестиции в деревопереработку и производство древесной продукции. Результаты позволяют утверждать, что инвестиции в лесное хозяйство и лесную промышленность служат важными факторами, определяющими объемы лесозаготовок в лесном секторе России, и наоборот. Вместе с тем, для благосостояния населения причинность выявлена лишь со стороны инвестиций в деревопереработку, что также практически важный результат в контексте политики стимулирования инвестиций в лесной сектор.
Ключевые слова: инвестиционная политика, лесная промышленность, инвестиции в лесной комплекс, причинность по Гренджеру.
Исследование выполнено за счет гранта Российского научного фонда № 19-18-00145, https://rscf.ru/project/19-18-00145/
Научная специальность: 08.00.05 - экономика и управление народным хозяйством.
Introduction
Russian forests represent a significant resource potential for the development of the timber industry. According to the Federal Forestry Agency, the total stock of standing timber in Russia is estimated at 82.5 billion m3, and the forest cover of the territory reaches 46.4 % in 2021. Against the backdrop of leadership in forest resources, Russia's performance on the world
market of timber products is somewhat more restrained: Russia occupies a leading position only in the production of low-processed wood products. The development path of the forest industry in the country is associated with a number of structural problems that include the low degree of wood processing, the lack of transport infrastructure for logging, the low efficiency of reforestation measures, the depletion and dete-
rioration of the resource base quality (Pyzhev, 2019; Gordeev, 2020; Antonova, 2017). At the same time, the issue of the efficiency of sectoral investments seems to be relevant.
In general, Russia's share in the world production of forestry products to some extent reflects the structure of the country's forest products exports: the share in roundwood is quite large, somewhat less in the production of sawn timber and wood-based panels, and in the production of pulp and paper products, Russia does not occupy a leading position (FAO, 2020). The low degree of wood processing is indeed one of the problems in the development of the Russian forest industry, which is explained, among other things, by the insufficiency of processing capacities. We also note that the added value of products is extremely small: the total share of the forestry sector in the gross value added in recent years has not exceeded one percent. According to the RF Customs Service, the share of timber and pulp and paper products in the structure of Russian merchandise exports ranges from 3.5 % for non-CIS countries to 5 % for CIS countries in 2020.
Moreover, the nature of forest management and logging that has developed in Russia in recent years has led to a significant depletion of primary forests and, accordingly, a decrease in the quality of wood: the assortment structure of the forest stand is significantly deteriorating. Thus, timber processing industries are faced with the inability to provide themselves with high-quality wood, which leads to a decrease in the number of enterprises in the industry.
Another group of problems is related to the low efficiency of reforestation, which should be dealt with by both government agencies and logging companies. In the last two decades, the volume of reforestation in Russia has had a downward trend (Pyzhev et al., 2015). For a long period, extensive exploitation of coniferous forests was carried out, while the process of reforestation was not given the required attention. One of the consequences is the change in the species composition of the forest stand from a "quality" coniferous forest to mixed forests with a predominance of deciduous wood.
Finally, we note the problems associated with the low level of timber processing and,
accordingly, the low value-added of timber products. Here it is also worth mentioning the problem of illegal logging associated with the export of unprocessed timber, which not only causes direct economic damage in the form of loss of forest resources and lost profits for the state, but also leads to serious consequences for the forestry itself (Pyzhev et al., 2015; Blam et al., 2011). In addition, illegal logging exacerbates the critical problem of inefficient reforestation.
Thus, for successful competition in the global forest products market, the development of the Russian forestry sector requires an integrated approach to solve the problems stated above. The measures applied to overcome the various constraints on the development of the forestry sector should be coordinated among themselves. Within that approach, improving Russia's position in the global market for forest products is possible only if we stimulate the creation and development of processing industries aimed at creating products with high added value, which requires attracting a significant amount of investment in the forest sector.
The purpose of this study is to investigate the causal relationships between timber investments and logging volume in Russia. To determine the causal relationships, we use vector autoregression (VAR) models and the Granger-causality testing method.
The rest of the paper is organized as follows. The article introduces the forestry sector development issues in Russia, focusing on sectoral investment; provides a literature review of the research on causal relationships; proceeds to explain the methodology and data; presents the results of empirical VAR modeling; and draws conclusions about causes and effects of investment and logging as well as welfare of population.
Literature review
and theoretical framework
One of the serious restrictions on the development of the timber industry in Russia is weak investment activity. The industry is considered unattractive for investors due to high risks and low profitability. The quality of institutions is also an important factor in invest-
ment attractiveness and investment efficiency. That is why the problem of insufficient investment in the development of wood processing facilities is extremely urgent.
Some measures are being taken to stimulate the development of the forest industry and its infrastructure and to increase the processing of wood products. To transfer the industry from exporting round timber to deep processing, a ban on the export of unprocessed and roughly processed wood of coniferous and valuable deciduous species is introduced from 2022. The effectiveness of this measure is not yet clear. The export of minimally processed wood will still not be banned, and timber with a moisture level not exceeding 22 % will not be banned at all. Nevertheless, a positive economic effect from the development of wood processing industries is expected in any case, the only question is how great it will be.
To stimulate the development of wood processing industries, the mechanism of state support for investment projects in the field of forest development seems to be a promising tool. Such a mechanism involves the provision of a preferential rate for the lease of forest plots to investors and the provision of forest plots without an auction in exchange for an obligation to create or modernize timber processing infrastructure facilities.
This measure is considered very promising; however, its implementation is associated with several problems. Over the entire period of state support for such projects, almost a third of the projects were excluded from the priority list. The reasons for exclusion are most often related to non-fulfillment of obligations, the bankruptcy of the applicant enterprises, lack of investment, or non-payment for the lease of forest plots. Such precedents, among other things, cause enormous damage to forests: investors cut timber at very low rates of payment, and often do not carry out measures for reforestation and protection of forests from fires. Considering the losses from the support of projects of unscrupulous investors, we consider it relevant to study the parameters that could make it possible to predict the likelihood of successful implementation of an investment project at the stage of deciding on its support.
The literature is rich in analyses of topics related to timber investment. Relevant topics are investment returns (Cubbage et al., 2020; Pra et al., 2019; Wang et al., 2014), investment to innovation in the forest sector (Weiss et al., 2021; Bouriaud et al., 2011), or specifically foreign investment (Qiu et al., 2021; Lin et al., 2020). However, the literature in these fields does not generally consider evaluating the efficiency of specific measures taken. There is also a lack of research that would suggest decision-making models for state support of investment projects. Logistic regression was used to create a credit rating model for construction and timber industries in Vietnam using an artificial neural network (Hai et al., 2018), which can be considered as a decision-making model for development and investment.
To assess the effectiveness of timber investments in the form of returns the commonly used indicators are Investment Rate (Sujova et al., 2015), Internal Rate of Return (Evison, 2018), and Net Present Value (Restrepo, Orrego, 2015). In relation to research on the forestry sector, there can be also used such indicators as Land Expectation Value which is a function of the forest price, forest yield, interest rate, and the plantation forests development costs (Restrepo, Orrego, 2015). However, these consider the efficiency of specific investment projects and do not evaluate the performance of measures taken by the government in general.
Many papers deal with problems related to stimulating investment in the forest sector. Instruments used by the government to stimulate investment include tax incentives (Nery et al., 2019), government subsidies, and cost-sharing programs (Song et al., 2014; Ovaska-inen et al., 2017). In our study on the analysis of global experience and its viability in Russia, we compiled a classification of best practices for stimulating investment in the forestry sector, considering country specifics (Ivantsova, 2020). We also found that the support measures which work in Canada are likely to be the most efficient ones under the national conditions because Canada runs similar forest land property rights. Canada practices R&D encouragement and public subsidies which could be imple-
mented in Russia as a type of public-private partnership or other types of joint investment of the projects in the forestry sector.
In order to develop an effective measure of stimulating investment in the timber industry, knowledge about the cause-effect relationships is crucially important. The further stages of the analysis include dataset description, unit root test, cointegration test, VAR modelling, and, finally, conclusions about Granger causality in selected variables.
Methodology and data
The sample data on 58 regions of Russia1 covers the period 2013-2016 and contains annual observations for logging volume, fixed capital investment in forestry and logging, wood processing, and pulp and paper production. To investigate a relationship between in-
vestment and human welfare we also collect data on Physical Volume Indices of Gross Regional Product (GRP). We assume that GRP reflects the growth of the economy, which in turn is reflected in the welfare of the population.
1 Some regions were excluded from the analysis due to lack
of data.
The data were collected from the Federal State Statistics Service (2022) through Unified Interagency Information and Statistical System (EMISS). This period was chosen due to data availability and is limited to 2016, as sectoral statistics are provided under the new classification of economic activities starting in 2017.
The description of selected variables is presented in Table 1 while summary statistics is displayed in Table 2.
To understand the dynamics of the variables under consideration, we also calculated the average values for the objects, which are presented in Table 3 and Fig. 1.
Similar dynamics is observed for the volume of logging and investment in wood processing and production of wood products. At the same time, investment in pulp and paper
production shows a decline in 2014 and 2016. Investment in forestry more than halved in 2014 compared to the previous year, but by 2016 recovered almost to its previous level. The dynamics of the GRP index is not pronounced, the changes are small, although it is worth noting that in 2015 the average growth took a value
Table 1. Set of variables
Variable Description Units
InvForestry Fixed capital investment in forestry and logging 1 000 000 RUB
InvWoodPr Fixed capital investment in wood processing 1 000 000 RUB
InvPaper Fixed capital investment in pulp and paper production 1 000 000 RUB
Logging Logging volume 1000 m3
GRPGrowth Physical volume indices of Gross Regional Product %
Table 2. Summary statistics
Mean Median Minimum Maximum
InvForestry 203,02 12,050 0,000 3117,4
InvWoodPr 702,69 83,300 0,000 10118
InvPaper 738,75 101,95 0,000 14185
Logging 3446,4 1550,6 10,827 35337
GRPGrowth 100,85 101,10 82,500 109,20
Table 3. Mean values for variables
2013 2014 2015 2016
InvForestry 282,878 135,478 149,405 244,328
InvWoodPr 481,998 629,369 734,231 965,148
InvPaper 708,529 564,141 850,419 831,905
Logging 3 263,642 3 430,900 3 468,606 3 622,369
GRPGrowth 101,510 101,666 99,981 100,240
-Logging,
- InvestmentWoodProcess,
- GRPGrowthlndex,
Fig. 1. Mean values of variables, 2013-2016
of less than 100, which means a slight decline in the economy.
Before exploring causal relationships with VAR modeling and Granger causality, we can consider the hypothesized relationships between the pairs of features under consideration. To do this, we present scatterplots for individual variables under study, which are presented in Fig. 2.
Scatterplot (a) reflects the relationship between investment in forestry and logging volume. We can see the positive nature of the dependence, with most of the values scattered at a low level of values of both variables. Scatterplot (b), reflecting the dependence between investment in wood processing and logging volume, and scatterplot (c), reflecting the dependence between investment in pulp and paper production and logging volume, show sim-
ilar relationships, though the decline is slightly different. According to scatterplot (d), the relationship between the volume of logging and the growth of GRP was not revealed. There are some outliers in the right in terms of logging volume, however. Here we proceed to explain the methodology of VAR modeling and causality testing.
We use VAR models to test for causality between investment, logging, and GRP. We also assume that all variables considered in this study are endogenous. The assumptions of VAR models require that the variables be stationary and not cointegrated. The most common way to test stationarity is the Augmented Dickey-Fuller test (ADF-test) with the null hypothesis that the series has a unit root against the stationarity hypothesis. The ADF-test is based on the evaluation of two regressions -
Fig. 2. Scatterplots for: (a) Investment in forestry and logging volume; (b) Investment in wood processing and logging volume; (c) Investment in pulp and paper production and logging volume; (d) Logging volume and GDP Growth
with a constant and with a constant and a trend, as further shown in equations (1) and (2) below:
AY = A, + yY-1 + et a)
AY=p0+p,t + yYM + ^ (2)
where AYt is the first difference of the series Yt; Y, P0 and P1 are coefficients to be estimated, and et is an error term.
Results
Stationarity (Unit Root Tests)
As already mentioned, the ADF test is employed to test the stationarity of the series. The ADF tests are performed on the level of initial observations by estimating the two models presented in equations (1) and (2).
The results of ADF-test are presented in Table 4. While the null hypothesis states that there is a unit root and the series is not station-
ary, we can conclude that the null hypothesis is rejected for all variables. Thus, the studied series are stationary and can be used for further analysis without additional transformations.
Since we have found that the series are stationary, no cointegration testing is required.
Granger causality (VAR)
To determine the optimal lag order in VAR model, we used Akaike's criterion (AIC), which indicated a lag order of 3. We estimated a system of five VAR equations for each of the variables.
To determine the causal relationships, we use F-tests of zero restrictions, where the null hypothesis states that the coefficients for all lags are zero. Thus, if the null hypothesis is rejected, we can assume that there is Granger causality for the variables indicated. The results of F-tests for each of the VAR equations
Notes: The optimal lag length was selected automatically using the Akaike criterion (AIC) for ADF test. "' Significant at 1 %; "significant at 5 % level.
Table 5. Results of F-test for VAR
Table 4. ADF-test statistics
Test with constant Test with constant and trend
InvForestry -4,90643*** -4,99681***
InvWoodPr -9,26623*** -9,28564***
InvPaper -4,03007*** -4,01304***
Logging -3,62495*** -3,76726**
GRPGrowth -13,9881*** -14,3738***
(3) InvForestry (4) InvWoodPr (5) InvPaper (6) Logging (7) GRPGrowth
InvForestry 31.641 [0.0000] 0.19709 [0.8983] 4.3047 [0.0057] 6.6145 [0.0003] 1.7326 [0.1613]
InvWoodPr 0.18866 [0.9040] 17.044 [0.0000] 0.21266 [0.8876] 0.11505 [0.9512] 4.1505 [0.0070]
InvPaper 11.706 [0.0000] 1.6966 [0.1688] 5.5908 [0.0010] 6.8061 [0.0002] 0.98739 [0.3996]
Logging 8.5973 [0.0000] 0.98868 [0.3990] 4.9844 [0.0023] 82.059 [0.0000] 0.55135 [0.6478]
GRPGrowth 0.68619 [0.5614] 0.31467 [0.8148] 0.47776 [0.6981] 0.98489 [0.4008] 0.36206 [0.7805]
R 2 0.553 0.253 0.271 0.748 0.111
Notes: [] parenthesis denotes p-value for F-statistics; the optimal lag length was selected using the AIC for VAR.
are presented in Table 5, where the columns are dependent variables, and the rows are factor variables.
In terms of Granger causality, under the assumption of endogeneity of the input variables, we found that logging volumes are the cause for investment in forestry and investment in pulp and paper production, and vice versa. The investment in forestry is also the cause of investment in pulp and paper production and vice versa. The only significant effect on GDP growth is of investment in wood processing and production of wood products. All statistically significant cause-and-effect relationships have positive directions of dependence. R 2 denotes quite high explanatory power for the equations (3) and (6). The explanatory power for equation (7) is expectedly low as there is only one significant determinant.
Conclusion
In this study, we attempt to explore causal relationships between timber investments and
logging volume in Russia. We estimated VAR model with five equations with an acceptable explanatory power. The main results show that: a) logging volume Granger causes investment in forestry and investment in pulp and paper production; b) investment in forestry and pulp and paper production Granger cause logging volume; c) investment in forestry Granger causes investment to pulp and paper production; d) investment in wood processing and production of wood products Granger causes the growth of GRP.
These results can be taken to suggest that investments in forestry and timber industry are important determinants of logging volume in the forest sector of Russia. And vice versa, the more the volume of logging is, the more investment in forestry and pulp and paper production flows. This conclusion is an extremely important practical result of the study in terms of stimulating investment in the creation of high value-added products in the timber industry.
References
Antonova, N. E. (2017). Transformaciya lesnogo kompleksa za gody rossijskih reform: Dal'nevo-stochnyj srez [Transformation of the forestry complex during the years of Russian reforms: Far Eastern section], In Prostranstvennaya ekonomika [SpatialEconomics], 3, 83-106. DOI: 10.14530/se.2017.3.083-106.
Anwar, M. A., Nasreen, S. & Tiwari, A. K. (2021). Forestation, renewable energy and environmental quality: Empirical evidence from Belt and Road Initiative economies, In Journal of Environmental Management, 291, 112684. DOI: 10.1016/j.jenvman.2021.112684
Blam, Yu. Sh., Babenko, T. I., Mashkina, L. V., Ermolaev, O. V. (2011). Ekonomicheskie posledstviya gosudarstvennogo regulirovaniya lesnogo sektora [Economic implications of public regulation in the forest sector], In Region: ekonomika i sociologiya [Regions: Economics and Sociology], 2, 211-222.
Bouriaud, L., Kastenholz, E., Fodrek, L., Karaszewski, Z., Mederski, P.S., Rimmler, T. (2011). Policy and market-related factors for innovation in forest operations enterprises. In Innovation in Forestry-Territorial and Value Chain Relationships. CAB International: Oxon, UK, 276-293.
Cubbage, F., Kanieski, B., Rubilar, R., Bussoni, A., Olmos, V.M., Balmelli, G., Mac Donagh, P., Lord, R., Hernández, C., Zhang, P., Huang, J., Korhonen, J., Yao, R., Hall, P., Del La Torre, R., Diaz-Balteiro, L., Carrero, O., Monges, E., Thu, H.T.T., Frey, G., Howard, M., Chavet, M., Mochan, S., Hoeflich, V.A., Chudy, R., Maass, D., Chizmar, S., Abt, R. (2020). Global timber investments, 2005 to 2017, In Forest Policy and Economics, 112, 102082. DOI: 10.1016/j.forpol.2019.102082
Dlamini, T. S., Fraser, G. C. G. (2010). Foreign Direct Investment in the Agriculture Sector of South Africa: Do GDP and Exports Determine Locational Inflows? In Studies in Economics and Econometrics, 34:2, 57-68. DOI: 10.1080/10800379.2010.12097202
Evison, D. C. (2018). Estimating annual investment returns from forestry and agriculture in New Zealand, In Journal of Forest Economics, 33, 105-111. DOI: 10.1016/j.jfe.2018.06.001.
Federal State Statistics Service of Russia (2022). Edinaya Mezhvedomstvennaya Informatsionno-Spravochnaya Systema EMISS [Unified Interagency Information and Statistical System EMISS]. Available at: https://www.fedstat.ru/ (accessed on 20 February 2022).
Food and Agriculture Organization of the United Nations (2020). FAO Yearbook of Forest Products 2018. Available at: https://www.fao.org/documents/card/en/c/cb0513m (accessed on 20 February 2022).
Gordeev, R. (2020). Comparative advantages of Russian forest products on the global market, In Forest Policy and Economics, 119, 102286. DOI: 10.1016/j.forpol.2020.102286
Hai, P. Q., Ngoc, T. T. L., Phuong B. D. T., (2018). An Alternate Internal Credit Rating System for Construction and Timber Industries Using Artificial Neural Network. Case Study of Bank for Development and Investment. In Econometrics for Financial Applications, Studies in Computational Intelligence, 760, 752-790. DOI: 10.1007/978-3-319-73150-6_59
Ivantsova, E. D. (2020). Mekhanizmy stimulirovaniya investicij v lesnoj sektor ekonomiki: analiz mirovogo opyta v kontekste celesoobraznosti ego primeneniya v Rossii [Investment encouragement mechanisms in forestry sector: Analysis of global experience and its viability in Russia], In VestnikPermskogo uni-versiteta. Seriya «Ekonomika» [Perm University Herald. Economy], 15(4), 566-586. DOI: 10.17072/19949960-2020-4-445-457
Laaksonen-Craig, S. (2004). Foreign direct investments in the forest sector: implications for sustainable forest management in developed and developing countries, In Forest Policy and Economics, 6, 359370. DOI: 10.1016/j.forpol.2004.03.011
Laaksonen-Craig, S. (2008). The Determinants of Foreign Direct Investments in Latin American Forestry and Forest Industry, In Journal of Sustainable Forestry, 27:1-2, 172-188. DOI: 10.1080/1054981080222 5275
Lin, B., Du, R., Dong, Z., Jin, Sh., Liu, W. (2020). The impact of foreign direct investment on the productivity of the Chinese forest products industry, In Forest Policy and Economics, 111, 102035. DOI: 10.1016/j.forpol.2019.102035.
Nery, T., Polyakov, M., Sadler, R., White, B. (2019). Spatial patterns of boom-and-bust forestry investment development: A case study from Western Australia, In Land Use Policy, 86, 67-77. DOI: 10.1016/j. landusepol.2019.04.015.
Ovaskainen, V., Hujala, T., Hanninen, H., Mikkola, J. (2017). Cost sharing for timber stand improvements: Inducement or crowding out of private investment? In Forest Policy and Economics, 74, 40-48. DOI: 10.1016/j.forpol.2016.10.014.
Pra, A., Masiero, M., Barreiro, S., Tomé, M., Martinez De Arano, I., Orradre, G., Onaindia, A., Brot-to, L., Pettenella, D. (2019). Forest plantations in Southwestern Europe: a comparative trend analysis on investment returns, markets and policies, In Forest Policy and Economics, 109, 102000. DOI: 10.1016/j. forpol.2019.102000
Pyzhev, A.I. (2019). Vliyanie rezhima sobstvennosti na lesa na effektivnost' lesopol'zovaniya: me-zhstranovyj analiz [Influence of the forest ownership regime on the efficiency of forest management: a cross-country analysis], In ZHurnal institucional 'nyh issledovanij [Journal of Institutional Studies], 11 (3), 182-193. DOI: 10.17835/2076-6297.2019.11.3.182-193.
Pyzhev, A. I., Pyzheva, Yu. I., Zander, E. V. (2015). Lesnaya renta v ekonomike Rossii: otsenka i effek-tivnoe ispol 'zovanie [Forest rent income in the economy of Russia: Evaluation and efficient application]. Krasnoyarsk, Sib. feder. un-t Publ., 202 p.
Qiu, S., Wang, Z., Geng, S. (2021). How do environmental regulation and foreign investment behavior affect green productivity growth in the industrial sector? An empirical test based on Chinese provincial panel data, In Journal of Environmental Management, 287, 112282. DOI: 10.1016/j.jenvman.2021.112282
Restrepo, H. I., Orrego, S. A. (2015). A comprehensive analysis of teak plantation investment in Colombia, In Forest Policy and Economics, 57, 31-37. DOI: 10.1016/j.forpol.2015.05.001.
Song, N., Aguilar, F. & Butler, B. (2014). Cost-share program participation and family forest owners' past and intended future management practices, In Forest Policy and Economics, 46, 39-46. DOI: 10.1016/j. forpol.2014.06.003
Sujova, A., Hlavackova, P. & Safarik, D. (2015). Analysis of Investment Effectiveness in the Wood Processing Industry of the Czech Republic, In Procedia Economics and Finance, 26, 382-388. DOI: 10.1016/S 2212-5671(15)00868-0.
Wan, M., Toppinen, A. & Hanninen, R. (2010). Econometric Analysis of China's Plywood Market, In Forest Products Journal, 60 (7-8), 679-687. DOI: 10.13073/0015-7473-60.7.679
Wang, Y., Bai, G., Shao, G., Cao, Yu., (2014). An analysis of potential investment returns and their determinants of poplar plantations in state-owned forest enterprises of China, In New Forests, 45, 251-264. DOI: 10.1007/s11056-014-9406-z
Weiss, G., Hansen, E., Ludvig, A., Nybakk, E., Toppinen, A. (2021). Innovation governance in the forest sector: Reviewing concepts, trends and gaps, In Forest Policy and Economics, 130, 102506. DOI: 10.1016/j.forpol.2021.102506
Zambrano-Monserrate, M. A., Carvajal-Lara, C., Urgilés-Sanchez, R., Ruano, M. A. (2018). Deforestation as an indicator of environmental degradation: Analysis of five European countries, In Ecological Indicators, 90, 1-8. DOI: 10.1016/j.ecolind.2018.02.049
