Enhancing the well-being of Europen mountain regions … · Enhancing the well-being of Europen...

Enhancing the well-being of Europen mountain regions by innovative governance models:

The Carbon forestry CPR regime

Tatiana Kluvankova, Michal V. Marek, Stanka Brnkalakova,

Urban Kovac,

ISEE Internatinal Conference Washington DC 26-29 June 2016

1) the importance of mountains to provide

climate regulation (ecosystem service) has

been overlooked long time

3) sectoral approach and lack of coordination

between global, international, national level

2) un-sustainable land use management

resulting in marginalisation of mountain areas

Climate change mitigation as challenge for well-being of mountain SES?

= the need for integrative approaches, innovations

and adaptative management of mountains is

recognized as a critical component of transition of

socio-ecological systems towards sustainability

Mountain regions as socio-ecological systems: challenges and opportunities -

Climate Regulation in European Mountain SES

• European mountains – 35% of surface

• Carbon stocks:

Figure 1: World carbon stocks in soil organic matter

(Schlessinger, 1999 adopted by Marek, 2014)Figure 2: World carbon stocks in terrestrial vegetation

(Schlessinger, 1999 adopted by Marek, 2014)

Global Change Research Centre AS CR, v.v.i

CO2

CO2

CO2

Global carbon

cycle

of the planet

Earth– crucial cycle of

our current

biosphere

This cycle

IS STRONGLY

RELATED TO THE

ASSIMILATION

ACTIVITY OF

PRIMARY

PRODUCETNTS

CO2

Global Change Research Institute AS CZ.

Opportunities of EU mountain regions?

1. Carbon forestry as technological innovation

2. Common pool resource regime as social innovation

to address climate policy objectives and

well being of mountain regions

Carbon Forestry PHILOSOPHY

Manage to conserve carbon, as well as to provide other services

Minimize carbon losses modify site preparation - ploughing, mounding, burning etc.

Prefer less invasive harversting means

preserve thinnings and harvest residues, or use for energy substitution

Maximize carbon gain develop a carbon based silvicultural system

fertilize (but be wary for N2O emissions)

preserve wind throw, encourage natural regeneration

Carbon fluxes in relation to the forest stand development

CC

CC

C

C

Ústav výzkumu globální změny AV ČR, v.v.i.Global Change Research Institute AS CR

Carbon EFFLUX

Carbon UPTAKE


CARBON FORESTRY- forest management focused on the LONG-TERM INCREASE of forest stands carbon storage

MINIMIZE CARBON LOSSES

MAXIMIZE CARBON GAIN

REGARDING FOREST STANDS POTENTIAL FOR CARBON STORAGE,

IT POSSIBLE TO IDENTIFY NEW FORM OF FOREST ECOSYSTEM SERVICE,

i.e. FOREST AS LONG-TERM DEPONIA OF CARBON

What about the basic principles of „CARBON FORESTRY„?????


Global Change Research Institute AS CR


FOREST SITE PREPARATION

Important points of forest management practice

influencing forest stand carbon storage

THINNING

HARVEST



FOREST SITE PREPARATION

Crucial point: to use only that kind of approaches,

which are able to minimize any injury of soil surface to

prevent any effluxes of carbon from the soil.



Essential forest management practice which is

responsible for far-reaching impact on forest

stand carbon storage

THINNING



FINAL step of silvicultural management

resulting in carbon export form the stand and

cessation of carbon capture. The used harvest

approach strongly affects the time, when clear

area

is the source of the carbon

HARVEST


Common pool resource regime

• CPR – collective self-organised regimes are capable ofcrafting own rules that allow for the sustainable andequitable management of SES. Moreover, due to their self-organisation and self-management, such regimes are able tosolve the resource management problems without externalauthorities (Ostrom, 1990, 1998, 2005, 2006, 2010)

protection

benefits

• Group of rights: access, withdrawal,

management shared among

owners/users.

• Evolved historically during medieval

Europe and land use reforms or are

formed as community forest (Scotland)

• Ecosystem dynamics (forest renewal)

is considered in harvesting and

management strategies.

• Management rules are derived and

operated on self-management and

collective actions - aiming to improve

the group’s conditions (Ostrom

conditions of robust regime).

FOREST COMMONS AND COMMUNITY

FORESTRY IN EVOLUTION IN EUROPE

8 SES in 4 countries: BULGARIA - private/state regimeSLOVAKIA – state/traditional forest CPR regimeSCOTLAND – private/new forest CPR regimeSLOVENIA – traditional/state regime

KluvankovaT, Brnkalakova S, Marek M.V, Valatin G, Hopkins J, Kovac U, Nijnik M,

Udovc A, Ambrose-Oji B, Zhiyanski M, Glushova M.: CARBON SEQUESTRATION FOR THE

WELL-BEING OF EUROPEAN MOUNTAIN REGIONS, In Climate Research in review.

Potential of carbon sequestration to enhance sustainable forest management for

welll being of mountain regions and scale down global CO2 objectives from the EU to

local policy arenas

CROSS COUNTRY COMPARISON

Methodology

1: Carbon sequestration potentialBaldocchi at al. 1988, CzechGlobe – ICOS-CzeCOS network: eddy-covariance technique

2. Expert assessment of intensity of carbon forestrymanagement practices

3. Social valuation

KluvankovaT, Brnkalakova S, Marek M.V, Valatin G, Hopkins J, Kovac U, Nijnik M,

Udovc A, Ambrose-Oji B, Zhiyanski M, Glushova M.: CARBON SEQUESTRATION FOR THE

WELL-BEING OF EUROPEAN MOUNTAIN REGIONS, In Climate Research in review.

Carbon capture capacity of different ecosystem types

Source: CzechGlobe –ICOS-CzeCOS (un published) measured in 2005-2010

ECOSYSTEM TYPE

CARBON CAPTURE [tC ha-1

year-1]

montane spruce stand up to 8

montane beech stand up to 7.4

highland monoculture spruce stand up to 5

agro-foresty system –poplar up to 4.7

agro-system (added energy not included) up to 4

wetland up to 3

montane non-managed grassland up to 2.5

Expert assessment of intensity of carbon forestrymanagement practices

MANAGEMENT PRACTICES

CASE AREAS

Bulgaria Scotland Slovakia Slovenia

state

regime

private

regime

private

regime

new

forest

common

s

state

regime

tradition

al forest

common

s

state

regime

tradition

al forest

common

s

1. site preparation

burning low low low very low very low very low very low very low

planting seedlings in a regular way (digging hole and

planting bought seedling)very high very high high high very high very high very high very high

soil preparation for seeds (natural regeneration -

digging holes before winter to make good conditions

for seeds germinating )

very low low very low very low very low low very low low

2. planting management

weeding after 1-2 years, mowing weeds - biomass

from weeds is taken awayvery low low very low very low very low very low very high very high

weeding after 1-2 years, mowing weeds - biomass

from weeds stay on soilvery low low very low high very high very high very low very low

3. thinning selected cutting very high very high low high very high very high very high very high

4. harvesting

clearcuts without left organic matter very low very low very low very low very low very low very low very low

clearcuts with left organic matter very low very low high high low low very low very low

strips/squatters/cycles (specify) of trees harvested high high very low very low high high very low very low

selected cutting – harvesting very high very high low high low very low very high very high

untouched area high high high very low very low low very low low

5. timber skiddingharvestors very low very low very low high low very low low low

tractors high high high high high high very high very high

cable yarding low low very low very low very high very low low low

horses very high very high very low very low low high very low very low

Carbon gains

Carbon losses

4. Potential of carbon sequestration in European mountain regions

Case areas

Bulgaria Scotland Slovakia Slovenia

state

regime

private

regime

private

regime

new

forest

commons

state

regime

traditional

forest

commons

state

regime

traditional

forest

commons

Total area (ha) 741.8 103,989 138,106.56 675 5,410 3,831.7 4,835 2,508

Carbon

capture

(tC/ha/year) 5.74 7.42 3.31 4.78 7.98 7.88 5.11 7.69

Carbon sequestration potential in selected SES (tC/ha/year)

We calculated carbon sequestration potential (CSP) based on the prevalent

ecosystem types as a function of aggregated carbon capture (tC/ha/year) of

the respective ecosystem type multiplied by its area:

CSP = Σ (A * Cc) where

Cc is the potential magnitude of the carbon capture of a given ecosystem type

presented in table X

A is the area of each ecosystem type

Baldocchi at al. 1988, CzechGlobe – ICOS-CzeCOS network: eddy-covariance

technique

4. Potential of carbon sequestration in European mountain regions

• Statistical approaches can readily identify the influence of independent

variables, and also provide a degree of confidence regarding their

contribution.

• interpretation and understanding of spatial-temporal processes of forest

management dynamics that is provided by statistical analysis is

essential for defining the best forest managment practices as driving

forces and predicting future carbon capture depending on given forest

regime management.

• statistical purpose of this study is to understand the relations between

the forest managemnet regimes and CS. From statistical aspect, this

paper describes how the key quantitative and qualitative variables of the

given forest cases affects to carbon sequestration potential , and how

these formulas could be used in the development of statistical

exploratory model.

Statistical model

• use quantitative and categorical variables for statistical analysis to

eximine the effect of the forest management regime linked with carbon

sequestration ptential.

• Quantitative variables comprises 8 European forest case areas in which

is identified its size in ha (scale by logaritmus), its difference between

maximum and minimum elevation (scale by logaritmus), type of forest

and carbon capture.

• The first set of categorical variables represent a forest regime type

where state-centralized regime is taken as reference level, and other

regimes is represented via dummy variables.

• The otherset of categorical variables is based on previous table, in

where we create dichotomous categorical variables for each

management practice with state low (very low and low) and state high

(high and very high). Low state is set as the reference level for each

management practice.

Statistical model

• We carried out PCA analysis in three cases.

• In the first case, we analyse only quantitative variables.

• In the second case, it is accounted for both quantitative and forest

management regime dummies, and

• in the last case we added also management practices dummies based

on performance of management model.

Statistical results

Statistical results

• For each case is valid that the first two components have inertia

(eigenvalue) greater than 1, and summarize 94.83% of the total

variance in case 1, 92.29% of the total variance in case 2 and 79.84%

of the total variance in the last case.

• In the case 1 quantile obtained for 8 individuals and 5 variables is worth

82.3%, the percentage of given data is 94.83% obtained from 8 individuals

which is higher.


70.7%, the percentage of given data is 92.29% obtained from 8

individuals which is higher.


less than 58%, but percentages from Table A.3 in Husson et.al (2010) are

decreasing with the increasing number of individuals and variables. The

percentage of given data in the last case is 79.84% obtained from 8

individuals which is higher than pearcentage obtianed from table.

• All three PCA anlysis cases should be based on the first two principal

components, since both the first and second component inertia are greater

than 1 and suggested tests confirmed that. First two dimensions express

significant amount variance of forest quantititative and qualitative

characteristics.

Statistical results

The scatter plot shows the

dependence of the first two

principal components. Data

points are labeled with an

assigned number for each

forest area. The scatter plot

illustrates the plane of the

first two components for the

first case

Statistical results

-0.6 -0.4 -0.2 0.0 0.2 0.4

-0.6

-0.4

-0.2

0.0

0.2

0.4

PC1

PC

2

1

2

3

4

5

6

7

8

-4 -2 0 2 4

-4-2

02

4

CC

AreaLog

ElevDiffLog

Coniferous

Broadleaves






forest area. . The scatter plot

illustrates the second PCA

case and

Statistical results

-0.6 -0.4 -0.2 0.0 0.2 0.4

-0.6

-0.4

-0.2

0.0

0.2

0.4

PC1

PC

2

1

2

3

4

5 6

7

8

-4 -2 0 2 4

-4-2

02

4

CC

AreaLogElevDiffLog

Coniferous

Broadleaves

Private

TradComm

NewComm






forest area. . The scatter plot

illustrates is the last PCA

analysis.

Statistical results

-0.4 -0.2 0.0 0.2 0.4 0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

PC1

PC

2

1

2

3

4

56

7

8

-4 -2 0 2 4

-4-2

02

4

CC

AreaLog

ElevDiffLog

Coniferous

BroadleavesPrivate

TradComm

NewComm

PM2_WATAPM2_WASS

T3_SC

H4_CWITHLOM

H4_SSCTH

H4_SCH

H4_UA

TS5_CY

TS5_H

TS5_H.1

• The first figure 1 illustrates the scatter plot between the first two principal

components with numbered forest areas. The forest areas denoted with

numbers 1 and 7 representing state forest management regime and 2 and

3 representing private forest management regime have very different forest

quantitative charecteristic vectors compared with the forest areas 6 and 8

located closed to CC (carbon capture) characteristic vector.

• It indicates that traditional commons forest management regime has more

positive effect to carbon catupture while state-centrilized or private

management are not aligned with that.

• Adding also management regime dimension into model is illustrated at the

2nd Figure where the same findings are confirmed as above.

• When management practices are added into model, shown in 3rd Figure

the variables vectors orientation changed wile the distribution of 8 forest

cases points distribution follows the same patterns as in (1.) and (2.) cases.

Forest case numbered with 5 is state-centralized regime in Slovakia that

has similliar properties as traditional commons forest case numbered with 6

based on historical context.

Statistical results

1. CPR as social innovation + 2. carbon forestry as technological innovation

= carbon forestry CPR regime

that could solve:

• growing vulnerability of mountain regions and global change aspects

• social dilemmas (individual short-term interest vs. social –long-term interest) = provision of public goods (multiple ecosystem services)

• contribution to well-being of margionalized European mountain regions

Some concluding remarks

• It has been shown that exploratory statistical analysis, capable of monitoring forest management practices patterns, estimates the potential of forest management regime effect to CSP.

• The proposed statistical methodology uses the dimension reduction technique - PCA as the predictive and indicative statistical tool.

• Exploratory spatial data analysis can discover the influence of each continuous variable but not systematic ranking.

• The results of PCA analysis demonstrated that first two principal components are sufficient to represent patterns in forest management regime linked with CSP.

Some concluding remarks

Thank you for your attention!

“ Complexity is not the same as chaos.”Elinor Ostrom (2009)

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