extfieldsofstudy: Economics Physics

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Research on the Influence of Carbon Tariff on China’s Energy Market Based on Computable General Equilibrium Model

In the process of continuous development of China’s economy, it has also brought great damage to the ecological environment. In recent years, China’s carbon emissions have ranked first in the world. Therefore, the collection of carbon tariffs will inevitably have a great impact on China’s energy market. Based on this, this paper first analyses the construction and expansion of the general equilibrium model, as well as the data and calibration of the general equilibrium model, then studies the numerical analysis of the impact of different carbon tariff pricing on high energy consumption goods, and finally analyses the impact of different carbon tariff pricing on China’s energy market.

Introduction
While China has made great achievements in economic development, it has also paid a large environmental cost, the most significant of which is that carbon emissions have become the first in the world [1]. As China's energy structure is still dominated by energy with high greenhouse gas emissions, the ecological environment is greatly affected. In addition, as the collection of carbon tariffs can help developed countries to maintain their competitiveness by setting up technical barriers to some extent, and it will pose a greater threat to China's export trade and reduce the competitiveness of China's manufacturing industry [2]. Therefore, the impact of carbon tariff on China's energy market is a problem worthy of attention. Fully understanding the impact of carbon tariff policy on China's energy market has become an urgent problem to be solved. Based on the general equilibrium model to calculate the impact of carbon tariffs, it can reflect the decision-making equilibrium of various micro entities in the economy [3]. The general equilibrium model is helpful to analyze the impact of carbon tariffs on carbon emission reduction, enterprise competitiveness and international trade [4]. Therefore, it is of great practical significance to study the impact of carbon tariffs based on the computable general equilibrium model on China's energy market. Figure 1. The general equilibrium model for calculating carbon tariff Among them, the production module adds energy synthesis and capital to get capital energy synthesis, and then adds it and labour to get capital energy labour synthesis, that is, the added value part of total output [6]. The foreign trade module uses two nested functions to investigate the impact of carbon tariffs on foreign trade. The income and expenditure module describes the income and expenditure status of each economic entity. In the general equilibrium of economy, the supply of goods is equal to the demand, and the hypothesis of labour and capital adjustment is adopted in the factor market. When calculating the carbon emission per unit commodity of various commodities, the carbon tariff module converts the energy input into the standard coal consumption through the standard coal conversion coefficient, and then calculates the carbon emission in the process of producing the commodity by using the coefficients shown in Figure 2 below.

Low heating coefficient
Carbon emission coefficient Oxidation rate Figure 2. Process of producing the commodity

General equilibrium model data and calibration
First of all, based on the pure exchange general equilibrium model of multi-regional products, this paper discusses the impact of carbon tariff on China's energy consumption goods and energy market, and introduces the extraction cost function used by energy exporting countries into this model. Model demand assumes that each country has a utility function covering commodity and global temperature changes. Secondly, the temperature change function is included in the model to reflect the relationship between temperature and carbon emission level, to show the changes of economic growth and emission intensity, so as to build a simple model. There is a correlation between the establishment of a possible policy intervention model and the use of national carbon pricing and border regulation measures. Different forms of carbon motivated import tariffs and export tax rebates exist. In addition, the energy technology labour force factor is introduced into the model to expand, so as to clarify the impact of energy technology labour force factor on carbon tariff policy. Energy technology needs specialized talents to operate, and mature technology will have a more significant resistance to carbon tariff policy.

General equilibrium model data
Model calibration and elastic parameters play an important role in the analysis process. The size of carbon pricing related to border adjustment measures will be used in numerical simulation analysis. After the carbon content of goods is specified, the relationship between border adjustment measures and trade barriers is in inverse proportion. In addition, emissions vary greatly between countries, so it is obviously not feasible to tax emissions produced by exporting countries.
It is not difficult to find that carbon incentive policies have little impact on the country through the calibration of carbon emissions and cross-border commodity flows. Border regulation measures are generally based on the comparison of domestic products of the importing country, rather than the implied carbon content in the imported goods. There are differences in the emission intensity between countries, and the carbon emission measures of domestic products of the importing country are in line with the border regulation measures.

The influence of different carbon tariff pricing on high energy consumption goods
Because of the particularity between departments, the influence of border tax adjustment is not without deviation, it will further affect the relative price. Compared with the impact on emissions, the adjustment of border tax has achieved the expected effect on the mitigation of carbon leakage, which also offsets the cost of emission reduction in countries that implement carbon pricing. Compared with the trade circulation without border tax adjustment measures, border tax adjustment reduces the import of the countries implementing the measures and increases the import of other countries. The value of products in the countries with carbon tax and related carbon pricing and border tax adjustments will increase, while the production value will decrease compared with the countries without border tax adjustments.

The impact of carbon tariff pricing on China's energy market
Based on the results of general equilibrium model, it is not difficult to find that the Levy of carbon tariff will greatly reduce the international competitiveness of China's energy consuming products, and the Levy of carbon tariff will further damage the competitive environment of international trade. Therefore, China's energy market will inevitably be impacted by carbon tariffs, which are mainly reflected in the following three aspects.

The influence of carbon tariff on energy intensity
The implementation of carbon tariff forces the transfer of production factors to low-carbon emission sectors, and substitutes capital and labour production factors for energy. The substitution effect of production factors on energy is limited. The tax rate of carbon tariff is directly related to the intensity of energy consumption, which is in inverse proportion. That is to say, the lower the tax rate of carbon tariff is, the faster the intensity of energy consumption will decline, which will have a serious impact on the normal operation of the whole economy.

The impact of carbon tariff on energy demand
The implementation of carbon tariff will lead to a sharp decline in energy demand. In the process of energy demand change, the innovation of energy technology will focus on the development of lowcarbon technology, so that the demand for all kinds of energy will be adjusted to a reasonable state in a short time, that is, the demand for high energy consumption resources will be reduced, while the use and demand for relatively low energy consumption resources will be increased.

The impact of carbon tariff on carbon emission reduction quota
Obviously, carbon tariff will lead to the improvement of China's carbon emission reduction rate, but at present, China is relatively backward in both carbon emission reduction technology and carbon emission reduction policy mechanism, so it will inevitably lead to some problems in the process of improving China's carbon emission reduction rate. Therefore, we can only promote the continuous improvement of carbon emission reduction rate through the technology research and development of energy technology-based labour force. In short, carbon tariff is not the best way to promote carbon emission reduction, but the key is to improve carbon emission reduction technology, so as to promote the achievement of carbon emission reduction measures.

Conclusions
In summary, with the increase of China's carbon emissions year by year, carbon tariff will inevitably have an important impact on China's energy market. Based on the general equilibrium model, this paper calculates the impact of carbon tariffs, mainly through the analysis of the impact of carbon tariffs on China's energy intensity, energy demand, and energy structure and emission reduction quota, to study the impact of carbon tariffs on China's energy market. Therefore, it should take necessary measures to deal with the impact of carbon tariff policy on China's energy market, ensure the stable development of China's energy industry, and promote the optimization and adjustment of energy industry structure.


Advances in Determining the Value and Importance of Useful Plants for Rural Populations in Santiago Del Estero, Argentina

The ecosystem services are components of nature, directly enjoyed, consumed, or used to yield human well-being [1]. Medicinal plant species constitute an important alternative to conventional medicine in a large number of developing countries, especially within poor communities that inhabit rural areas and lack access to health services [2]. Similarly, plants provide extra food supply for people living in poor regions. Nonmarket valuation, and other economic techniques that emphasize exchange values over cultural and ecological values, have been subject to criticism regarding the inability of exchange values to represent the total value of an ecosystem [3]. Many of these forest goods and services do not have a market price to reflect the benefits they provide to society. While several goods such as timber have market prices, or are at least partially traded in the market (such as game for hunting), most forest services are generally non-priced public goods (leisure and recreation, watershed protection, biodiversity, etc.) [4]. Especially in the context of the valuation of ecosystem goods and services, this is an unjustified oversimplification, as there are often trade-offs between the short-term and long-term use of nature. Thus, a suitable valuation method has to take into account irreversibility and long-term consequences. As a consequence, values are not static but dynamic and depend on the co-evolution of the three subsystems nature, economy and society [5]. Bruno and Sarmiento [6] investigated the economic dependence of people who inhabit forests with their services. Studies of indigenous and popular knowledge about natural resources are becoming increasingly important in defining strategies and actions for the conservation or recovery of forests. The key role of indigenous and local knowledge in biodiversity conservation and management has been consistently highlighted within many international directives [7]. The use of plants and plant products for different purposes such as food, wood, medicine, fiber, oil, fodder, aroma and ornament could be traced as far back as the beginning of human civilization [8]. Crimson Publishers Wings to the Research Research Article

MCDA.000642. 6(4).2020
Others studies on medicinal plant use have been conducted in several localities in Santiago del Estero, northern Argentina, by Díaz [9], Sarmiento et al. [10]. Regarding useful plants for food, Sarmiento et al. [11,12] published studies on consumers' tastes and preferences. He also did research on the knowledge of native forest food products in urban populations. The properties of dye plants were valued by Sarmiento and Guerrero Maldonado [13]. These studies and research were carried out within a thematic network of medicinal, aromatic and dye plants in northern Argentina, coordinated by Arraiza et al. [14]. Under the Ecosystem Services approach, ecosystems are considered to be important because of the multiple values they offer. They provide individual utility and economic development (i.e. instrumental value), help to build meaningful relationships between humans and nature (i.e. relational values), and serve as ends in themselves (i.e. intrinsic values) [15][16][17]. Therefore, ES valuations are crucial since they attempt to reflect these multiple values [18].

Objective
The objective of this paper is to examine the studies about valuation of ecosystem services provided from useful plants in Santiago del Estero, Argentina and to determine information gaps on this topic.

Material and Methods
The study was conducted in different rural areas in the province of Santiago del Estero. The province has a population of 874,006 inhabitants [19] and covers an area of 136.351km 2 , in northwestern Argentina. The study area lies between latitudes 20° to 30°S and longitudes 62° to 66°W. The inhabitants are mostly rural people with low incomes and far from major urban centers. Productive economic activities aimed at low-scale subsistence are predominant in most of these areas. Surveys were conducted personally and individually on a population accessed at random in the locations described, and trying to cover as many families as possible. In all the study cases the Contingent Valuation Method was applied. A similar survey was applied in all the sites studied. In the case of medicinal and aromatic plants, 17 surveys were performed in Santos Lugares, 30 surveys in Guampacha and 80 surveys on a sample population within the 18-82 age range, being women the majority [9,10]. Regarding dye plants, the sample population to which the survey was conducted was composed of thirty-five (35) people in the 16-78 age range, and corresponding entirely to women (no man was found dedicated to the craft with sheep wool or to the collection of dye plants) [13]. Between 2017 and 2018, the provision of food plant in others parts of the province were valued [20] with Contingent Valuation Method in 111 cases.

Result and Conclusion
Regarding medicinal species, there are only two other studies in the province and correspond to the western area departments. This work was the first carried out in the sampled areas and the results can be compared with the ones obtained in other studies and will help expand the database in Santiago del Estero (Table 1). Regarding plants for medicinal purposes, of the total respondents, 97.5% obtained them directly from the forest. In addition, 65% said to have cultivated a plant in their garden. Most people obtain the plants from the forest or grow them in their garden. The importance of the natural environment for these people as a source of alternative timber resources is demonstrated. The results can be the basis for researching the development of potential markets of medicinal plants in the region, as Khan and Abdur [21] comment. More research on the use of aromatic plants in Santiago del Estero should be done since there is no research results related to the valuation of aromatic plants in the province. Regarding dye species, there is no history of research in the province.

MCDA.000642. 6(4).2020
This document is the first of its kind in this area. The importance of the implementation of the method and the value found can generate future applications in similar situations. In Salta, Argentina, Lambaré et al. [22] studied and compared the use of dye plants between 1994 and 2000 with those in 2007 and 2008. They also analyzed the current factors in the acquisition and transmission of knowledge related to such use. In relation to rural people's perceptions of the value of natural resources, the healing uses they give to medicinal plants have become a natural heritage they are willing to protect and preserve for future generations. As for the economic value expressed in monetary terms, the environmental service of the plants studied represents money that people avoid spending on medical services due to the free availability of medicinal plants. If the values of Consumer Surplus and Surplus Compensatory calculated for the WTP and WTC are contrasted, it can be noted that the values obtained are very similar for both, although slightly higher than those calculated for WTA and WTP. Regarding food plants, there is no history of research in the province. This document is the first of its kind in this area. In view of the trends to value ecosystem services, it is necessary to find other values of ecosystem services such as the conservation of biodiversity, carbon capture or water quality in the region.


Using Mixed Integer Goal Programming in Final Yield Harvest Planning: A Case Study from the Mediterranean Region of Turkey

: A mixed integer goal programming model is developed to address the regeneration planning problems of even-aged forests in the Mediterranean region of Turkey. The unique aspect of the goal programming formulation is to minimize deviations in scheduled wood product volumes and the size of harvest areas within each time period, as these are important goals for the management area. About 98% of the forests in Turkey are considered even-aged, and 2% are uneven-aged. Therefore, an age class method is used for the planning of even-aged forests. For the areas where this method is applied, reaching the optimal age class structure is the ﬁrst priority. This involves implementing ﬁnal harvests (clearcuts) to regenerate an amount of forest area into each age class. To meet the local market’s needs, forest enterprises also require the ﬁnal yield to be fairly equal each year. Further, it is desired that the harvest area (regeneration area) is relatively equal each year, to address operational considerations. A linear goal programming model is developed to address the problem. The minimization of deviations from both the harvest area and harvest volume targets are incorporated as goals in the objective function of the model. Several scenarios are solved using the extended version of Lingo 16. A scenario with weights of 0.8 for area and 0.2 for volume produces the best results. Here, the total deviation for 20 years is 3.8 ha in area and 2889 m 3 in volume. In the actual regeneration plan, the area deviation for 10 years is 54.72 ha (6.2% of total regeneration area), and the volume deviation is 20,472 m 3 (9.8% of harvest volume). The model described through this study can be developed further and integrated into forest management planning software and processes used for the planning of even-aged forests in the Mediterranean region.

Introduction
During the last two decades, Turkey has experienced considerable economic, environmental, and social developments which have increased the demands and expectations for forest resources. Until recently, forests have been predominantly the source of wood production, however, now, they are viewed as a source of non-wood forest products and provider of ecological and sociocultural services. In line with these developments, Turkey has changed its classical forest management planning model and implemented a functional ecosystem-based planning approach [1]. Modern harvest planning is needed not only to ensure that a plan of action provided to foresters is efficient from an economic perspective, but also that it recognizes as many of the quantifiable management issues as possible to forest management objectives that relate to commodity production, wood flow, economic returns, and perhaps other objectives [37][38][39][40][41][42][43][44][45][46]. More recently, spatial considerations have been incorporated into a GP problem. For example, [14] developed mixed integer GP models for thinning block designation based on the distance between scheduled blocks, and [47] and [48] developed processes for aggregated forest harvesting activities on the basis of their geographic location.
As a problem-solving methodology, GP is very useful in understanding contrasts in outcomes from the use of different assumptions in forest management planning processes [49], yet in one review, only 14 papers were located describing the use of GP in forestry from the onset of its use through about 2015 [50]. Many GP problem formulations use decision variables that are assigned continuous real values or that utilize data structures that preclude the ability to control the harvest of adjacent stands, e.g., [51]; relatively few examples, e.g., [7,14,52] of GP in forestry have utilized integer decision variables and attempted to control the timing and placement of harvests. Others have developed pairwise adjacency constraints to maximize age contrast between stands [51] and maximize clustering of stands within defined time periods [47]. Furthermore, while other research has proposed alternative GP formulations, we feel the type of integrated forest management planning issue described here has yet to be described in the forest management literature.
The objective of this study is to develop and assess a mixed integer multi-objective GP model that addresses spatial forest planning problems involving time-space arrangements of regeneration sites. Previous work of ours in this area involved creating thinning blocks using a mixed integer goal programming process [14]. One hypothesis for the current study is that GP can address a type of integrated forest management planning issue described above, which has yet to be assessed in the forest management literature. Another hypothesis is that through the GP modeling effort, more efficient final yield harvest forest plans can be developed, as compared to actual forest plans that have been developed through other means. Along these lines, a linear regeneration model is developed. The model aims to regenerate an equal amount of forest area every year to schedule an equal amount of product. This includes regenerating neighboring sub-compartments to final harvests that are smaller than 5.0 ha in size in the same year. In accordance with legislation associated with the management of the study area forest, areas applying a final harvest cannot exceed 25.0 ha during the greening (green-up) period. With this study, we also introduce the use of the greening (green-up) period approach in the final yield harvest planning of Turkish pine (Pinus brutia Ten.) forests in the region.
Without using a decision support system such as the one we propose, it is difficult to schedule the regeneration of an equal amount of area each year and to avoid regenerating forest areas larger than the allowable clear-cut size during the greening period. In practice, the regeneration activities are conducted by local foresters using detailed silviculture plans produced from forest management plans. In the best situation, area deviation may be more than 5% of the total regeneration area each year, and the volume deviation may be around 10% of the harvest volume each year. However, these deviations are acceptable in Turkish forest management. Avoiding clearing larger areas is crucial for ecological and environmental issues such as soil conservation, wildlife protection, and biodiversity conservation.

Study Area
The study area is located within the Akoren Planning Unit, in the southern part of Turkey ( Figure 1). The study area contains 5380 ha of coniferous forests, where Turkish pine is the dominant tree species. Other coniferous tree species include Anatolian black pine (Pinus nigra Arn. subsp. pallasiana (Lamb.) Holmboe var. pallasiana), fir (Abies cilicica Carr.), cedar (Cedrus libani A. Rich.) and juniper (Juniperus spp.). The growing stock of the forest is 559,006 m 3 and the annual increment is 22,422 m 3 . The main management objective of this coniferous forest is pine wood production. Since even-aged forest management is applied, every year the same amount of forest area is being regenerated and the same amount of wood flow is desired from the regeneration cuts (clearcuts).
Since even-aged forest management is applied, every year the same amount of forest area is being regenerated and the same amount of wood flow is desired from the regeneration cuts (clearcuts).

Study Area Data
While Turkish pine is the dominant tree species in the study area, Anatolian black pine stands (36 ha) and hardwood-coniferous mixed stands (27 ha) are also present. Only 5366.5 ha of productive forest (crown closure is more than 10%) of the planning unit is considered in this research, and 13.3 ha of degraded forest is ignored. The majority of the forest (55%) is considered site class II (medium). In this study, the site class differences have been neglected. It should be noted that the thinning operations in Turkish forestry are conducted at the compartment level and the regeneration operations are conducted at the stand (sub-compartment) level.
According to the current forest management plan for the study area [53], the annual allowable cut from final harvests is 23,350 m 3 , arising from 1777 ha over 20 years (88.85 ha per year). Additionally, the number of sub-compartments (stands) planned for regeneration is 166. According to the Communiqué on Technical Principles of Silvicultural Applications published by the Turkish Forest Service [54], final harvest areas shall not be larger than 25 ha in even-aged forests that have a production function. Therefore, stands larger than 25 ha were subdivided in ArcMap. For this study, the green-up period was assumed to be five years. During this time, it was not desired to regenerate the two adjacent compartments that comprise together an area larger than 25 ha. Likewise, to avoid regeneration activities in very small areas, it is assumed that adjacent stands smaller than 5 ha could be harvested together in the same year.

Problem Formulation
For the regeneration (final harvest) problem, the deviations from the harvest area (regeneration area) and harvest volume (final yield harvest) targets should be minimized. The foresters also want to cut each stand as a whole when they are entered, therefore, the decision for a stand is discrete (harvest or do not harvest). Furthermore, adjacent final harvests need to be spatially and temporally controlled. The nature of the management situation suggests that mixed integer multi-objective GP would be applicable to solve this problem.
The objective function of the linear regeneration model minimizes the deviations from wood volume and regeneration area targets over a 20-year time horizon. The constraints of the model are area constraints that prevent work in both small and very large areas during the greening (green-up) period. The mathematical formulation of the model is as follows:

Study Area Data
While Turkish pine is the dominant tree species in the study area, Anatolian black pine stands (36 ha) and hardwood-coniferous mixed stands (27 ha) are also present. Only 5366.5 ha of productive forest (crown closure is more than 10%) of the planning unit is considered in this research, and 13.3 ha of degraded forest is ignored. The majority of the forest (55%) is considered site class II (medium). In this study, the site class differences have been neglected. It should be noted that the thinning operations in Turkish forestry are conducted at the compartment level and the regeneration operations are conducted at the stand (sub-compartment) level.
According to the current forest management plan for the study area [53], the annual allowable cut from final harvests is 23,350 m 3 , arising from 1777 ha over 20 years (88.85 ha per year). Additionally, the number of sub-compartments (stands) planned for regeneration is 166. According to the Communiqué on Technical Principles of Silvicultural Applications published by the Turkish Forest Service [54], final harvest areas shall not be larger than 25 ha in even-aged forests that have a production function. Therefore, stands larger than 25 ha were subdivided in ArcMap. For this study, the green-up period was assumed to be five years. During this time, it was not desired to regenerate the two adjacent compartments that comprise together an area larger than 25 ha. Likewise, to avoid regeneration activities in very small areas, it is assumed that adjacent stands smaller than 5 ha could be harvested together in the same year.

Problem Formulation
For the regeneration (final harvest) problem, the deviations from the harvest area (regeneration area) and harvest volume (final yield harvest) targets should be minimized. The foresters also want to cut each stand as a whole when they are entered, therefore, the decision for a stand is discrete (harvest or do not harvest). Furthermore, adjacent final harvests need to be spatially and temporally controlled. The nature of the management situation suggests that mixed integer multi-objective GP would be applicable to solve this problem.
The objective function of the linear regeneration model minimizes the deviations from wood volume and regeneration area targets over a 20-year time horizon. The constraints of the model are area constraints that prevent work in both small and very large areas during the greening (green-up) period. The mathematical formulation of the model is as follows: where, i years (1, 20) j objectives: 1 = area scheduled for harvest, 2 = volume scheduled for harvest w − ij weight for negative deviations in objective j, year i w + ij weight for positive deviations in objective j, year i d − ij negative deviation in objective j, year i d + ij positive deviation in objective j, year i k regeneration stands X ki binary (0, 1) decision variable representing the harvest of stand k during year i A ki area available for harvest in stand k during year i V ki volume available for harvest in stand k during year i X mi binary (0, 1) decision variable representing the harvest of stand m during year i Pr pi pairs of adjacent stands AC i total area scheduled for harvest in year i AT i area target in year i VC i total volume scheduled for harvest in year i VT i volume target in year i Equation (1) is the objective function, which minimizes the deviations from harvest area and harvest volume targets, which were 88.85 ha and 23,350 m 3 per year. Equation (2), together with Equation (9), forces a stand to be scheduled for harvest. Equation (3) represents the accounting rows to add up area scheduled for harvest. Equation (4) represents the accounting rows to add up the volume scheduled for harvest. Equation (5), together with Equation (10), forces adjacent stands k and m, that are smaller than 5 ha, to be scheduled for harvest in same year. Equation (6) ensures adjacent stands k and m not be scheduled for harvest during the greening period (5 years). Twenty equations, represented by Equation (7), determine deviations in area scheduled for harvest. Twenty equations, represented by Equation (8), determine deviations in the volume scheduled for harvest.
The problem was solved using the extended version of Lingo 16.0 [55]. This version of Lingo allows an unlimited number of continuous value variables, integer variables, and nonlinear variables. Table 1 displays the total number of variables and constraints of our model. The problem was solved using different weights for the deviations in harvest volume and harvest area that ranged from 0.0 to 1.0, in 10 percent intervals, which created 11 scenarios (Table 2). These weights were selected in order to observe the sensitivity of changes in the volume and harvest area (regeneration area) outcomes when emphasis on the objective function elements was altered. The software was operated on a PC with a 2.60 GHz Intel ® Core™ i7 processor and 16 GB of RAM. A maximum of about 500 million iterations was allowed for the regeneration model. The computing times by scenarios can be found in Table 3. Table 1. Total number of variables and constraints used in the models.

Variables and Constraints Linear Regeneration Model
Variables 3620 Nonlinear variables 0 Integer variables 3500 Constraints 2767 Nonlinear constraints 0

Results from the Regeneration Model
Using the regeneration model, Lingo was able to produce a feasible solution to the stated regeneration problem for each of the 11 management scenarios. Scenarios 1 and 11 are the reference scenarios for the regeneration area and harvest volume, respectively, because they represent the greatest weights applied to the area deviations (scenario 1) and the volume deviations (scenario 11). A total of 210 h and 4 min and 59 s was required to run all 11 scenarios. The longest computing time was 35 h and 39 min and 2 s (scenario 4).
In the results, the total deviation from the target value for the area reference scenario (scenario 1) was only 3.8 ha for 20 years and the total deviation from the target value for the volume reference scenario (scenario 11) was 1025 m 3 . When the area and volume deviations were evaluated together, scenario 3 provided the best results. When the area and volume deviations were evaluated individually, respectively, scenarios 1, 3, 2, and 5, for area and 8, 11, 9, and 6 scenarios for volume provided better results (Table 4). It is interesting that scenario 8, with weights of 0.3 for area and 0.7 for volume, produced a 874 m 3 deviation in scheduled volume, which is better than the reference scenario (scenario 11). We allowed a maximum of about 500 million iterations to solve the problem. If we allow more iterations, the volume reference scenario would produce a better result. The actual area scheduled for regeneration for all 11 scenarios is displayed in Table 5 and the deviations from the area target (88.85 ha) in Table 6. However, in addition to the results of area reference scenario (scenario 1), the results of scenario 3, 2, and 5, which gave better results respectively, are presented in Figure 2.     Since the stands represent real forest data, the sizes of stands are all different. Therefore, a solution showing an exact adherence to the target (i.e., with 0 deviation from the target) is likely impossible. The area scheduled for harvest under the area reference scenario (scenario 1) deviated from the target by only 3.8 ha. The deviations here ranged from −0.95 (19th year) to +0.65 (1st year) ha per year. When the area and volume deviations were evaluated together, the total deviation was only 3.8 ha for Forests 2020, 11, 744 9 of 17 20 years for scenario 3, which provided the best result. The deviations from the target value range from −0.55 ha (4th year) to +0.55 ha (9th year). In other words, the maximum deviation from the area target value was 0.55 ha (0.6% of the annual regeneration area). In Turkish forest management, this deviation is very well acceptable.
As stated earlier, the area to be regenerated annually in the study area is 88.85 ha (1777 ha/20 years). If local foresters decide to implement the results of scenario 3, they annually will regenerate a forest area ranging from 88.3 ha to 89.4. This means that the maximum deviation will be only 0.55 ha, which is 0.6 per cent of the annual regeneration area. The regeneration operations in Turkey are very costly and it is not easy to find experienced forestry workers to employ each year to conduct harvesting and regeneration activities. If this deviation increases, this means the forest enterprise regenerates forests in different area sizes each year. This will affect the annual budgeting issues, operational efficiency, the needs of local timber markets, and the employment opportunities for experienced workers. More importantly, natural regeneration requires considerable attention. Quality seed collected from the same forest, intensive vegetation and litter clearing within the harvest area, and light tillage of the ground may be needed. After regeneration activities are completed, other forestry operations should be conducted such as seedling counting, vegetation management, and seedling maintenance. The success of all these operations is related to the annual regeneration area size. If the annual deviation from this target is minimal, the forest enterprise will be much more organized to conduct these regeneration operations.

The Results for Volume
The actual volume scheduled for harvest for all 11 scenarios is displayed in Table 7 and the deviations from the volume target (23,350 m 3 ) in Table 8. In addition to the results of volume reference scenario (scenario 11), the results of scenario 8, 9, and 6, which gave better results respectively, are presented in Figure 3.   Similar to the stand area issue, since the stands represent real forest data, the sizes of the stands are all different. Thus, the volume available in a stand is different. Therefore, a solution showing an exact adherence to the target (i.e., with 0 total deviation from the target volume) is likely impossible. For the volume reference scenario (scenario 11), the total deviation from the volume target was 1025 m 3 over 20 years. The deviations here ranged from −158 m 3 (13th year) to +166 m 3 (10th year). The total deviation for 20 years was 2889 m 3 under scenario 3, which provided the best result if we evaluate area and volume goals together. The deviations in volume scheduled in scenario 3 ranged from −1.047 m 3 (1st year) to 276 m 3 (20th year), and the ratio of deviations between the target volume and the scheduled volume varied between −4.48% and +1.18% per year. Again, this deviation is also  Similar to the stand area issue, since the stands represent real forest data, the sizes of the stands are all different. Thus, the volume available in a stand is different. Therefore, a solution showing an exact adherence to the target (i.e., with 0 total deviation from the target volume) is likely impossible. For the volume reference scenario (scenario 11), the total deviation from the volume target was 1025 m 3 over 20 years. The deviations here ranged from −158 m 3 (13th year) to +166 m 3 (10th year). The total deviation for 20 years was 2889 m 3 under scenario 3, which provided the best result if we evaluate area and volume goals together. The deviations in volume scheduled in scenario 3 ranged from −1.047 m 3 (1st year) to 276 m 3 (20th year), and the ratio of deviations between the target volume and the scheduled volume varied between −4.48% and +1.18% per year. Again, this deviation is also acceptable in Turkish forest management. In order to better illustrate the results of the linear regeneration model, a map was prepared for scenario 3, which again seemed to be the best solution achieved (Figure 4).  As noted earlier, the 23,350 m 3 annual final yield was set as the volume target. If local foresters decide to use the results of scenario 3, they will harvest wood ranging from 22,303 m 3 to 23,626 m 3 each year. This means the maximum deviation will be 1047 m 3 in the first year and the minimum deviation will be only 3 m 3 in the third year. If they prefer to use the results of scenario 8, the total volume deviation for 20 years will be only 874 m 3 . In this case, the regeneration area deviation will be 34.70 ha for 20 years, ranging from 4.05 ha (1st year) to −5.65 ha (16th year). Since this study doesn't evaluate the economic results of the management scenarios, we are not recommending the local foresters which scenario they would decide to use. However, the largest income source of forest enterprises in Turkey are timber sale revenues. Furthermore, around 90 per cent of the timber they produce comes from the final yield harvest (regeneration). In this case, minimizing the volume As noted earlier, the 23,350 m 3 annual final yield was set as the volume target. If local foresters decide to use the results of scenario 3, they will harvest wood ranging from 22,303 m 3 to 23,626 m 3 each year. This means the maximum deviation will be 1047 m 3 in the first year and the minimum deviation will be only 3 m 3 in the third year. If they prefer to use the results of scenario 8, the total volume deviation for 20 years will be only 874 m 3 . In this case, the regeneration area deviation will be 34.70 ha for 20 years, ranging from 4.05 ha (1st year) to −5.65 ha (16th year). Since this study doesn't evaluate the economic results of the management scenarios, we are not recommending the local foresters which scenario they would decide to use. However, the largest income source of forest enterprises in Turkey are timber sale revenues. Furthermore, around 90 per cent of the timber they produce comes from the final yield harvest (regeneration). In this case, minimizing the volume deviation is important to meet the needs of the markets in a sustainable manner.

Comparison of the Actual Regeneration Plan Data with the Results of the Regeneration Model
The actual regeneration plan [56], or detailed silviculture plan, for the study area indicated that over 10 years 881.8 ha of forests would be regenerated, and 209,600 m 3 of wood material would be produced (Table 9). This plan was developed using less sophisticated mathematical methods but took into account the growth of the forest. Given this information, we assumed that an area of 88.18 ha (881.8 ha/10 years) and a volume of 20.960 m 3 (209,600 m 3 /10 years) would be scheduled each year, since finer detail was unavailable from the plan. Using scenario 3 of the regeneration model, the total deviation for the first 10 years was 2.1 ha in the area (0.24% of total 10-year regeneration area) and 1959 m 3 in volume (0.84% of total 10-year harvest volume). Using the guidance from the actual regeneration plan, the area deviation would be 54.72 ha (6.2% of total regeneration area) from the assumed area target, and the volume deviation would be 20,472 m 3 (9.8% of harvest volume) from the assumed volume target (Table 10). This comparison suggests some efficiencies in the regeneration program can be realized using the mathematical programming approach.

Discussion
As all of the forests in Turkey are managed using plans prepared in consideration of basic principles such as sustainability, economics, productivity, multi-purpose utilization, protection of biological diversity, aesthetics and protection of other landscape values, and carbon balance, the use of appropriate techniques to achieve management objectives is important. Final yield and intermediate yield harvest plans are included in these management plans. The regeneration activities envisaged in the management plan are carried out according to detailed silviculture plans. In the long run, in order to harvest an equal amount of wood every year, it is necessary to cut a relatively equal area of forest area that will provide this each year. This is only possible in forests that are optimal in terms of their age class distribution. The way to meet these requirements is to establish an equal area of new stands every year and to manage them to the end of the rotation length. This approach, often considered as a regulated forest, should be taken into consideration when preparing the detailed silviculture plans. Furthermore, in accordance with legislation, areas applying a final harvest cannot exceed 25 ha during the greening (green-up) period. It seems impossible to regenerate an equal amount of area each year and to avoid regenerating forest areas larger than 25 ha during the greening period without using decision support systems. We have demonstrated in this case that a mixed integer GP model can be developed to provide efficient plans which can be used as guidance for implementation of activities by field foresters.
With a total of 3620 variables, the linear regeneration model presents a difficult problem. Both this situation and the use of 500 million iterations have greatly extended the Lingo's computing time. It took a total of 210 h and 4 min and 59 s to run all 11 scenarios. The longest computing time was 35 h and 39 min and 2 s (scenario 4). One of the factors directly affecting the computing time is undoubtedly the number of adjacency constraints, therefore one of the drawbacks of the model was that its long computing time needed to provide proper results when the adjacency constraints are numerous. While there is an inverse relationship between the clear-cut area size and the number of constraints, there is a linear relationship between the green-up period and the number of constraints. Conversely, as the green-up period increases, so generally does the number of constraints. Others recommend that the clear-cut size area be as large as possible and that the green up period is as short as possible to reduce the number of adjacency constraints [57].
We also explored the sensitivity of the model to produce the goals desired, by assessing how the results (harvest volume and regeneration area) changed when the weights applied to the deviational values varied. Managers seeking to locate forest planning solutions may desire that two (or more) goals have the same relative weight, and therefore one challenge to the planner is to determine the normalized weights that are appropriate for the problem [14]. A major issue of debate among the researchers using G concerns the use of normalization techniques to overcome incommensurability. Incommensurability in a weighted GP occurs when deviational variables are represented by different units (ha, m 3 , km, etc.). The summation of different units may cause an unintentional bias towards the objectives that have a larger magnitude. To overcome this difficulty, it may be necessary to divide each objective by a constant pertaining to that objective, to ensure that all objectives have roughly the same magnitude. Such a constant is known as a normalization constant. There are several normalization methods, each with its own normalization constant [58]. Some of them are percentage normalization [59], Euclidean normalization [60,61], summation normalization [62], and zero-one normalization [63]. However, these normalization methods cannot guarantee that the achieved objectives are consistent with their goals [64]. Therefore, with regard to the type of forestry problem addressed in this research, this area of investigation is still open.

Conclusions
In this study, we explained how one might use mixed integer GP in the Turkish forest management planning system to address final harvest issues. This study introduced a new approach to regeneration planning. This study provided an enhancement on previous research in this area as well. The previous research proposed an approach in intermediate harvest planning and stand tending block designation using GP models. The compartments were grouped together to create blocks by minimizing deviation between scheduled compartments. It was the first application of GP in intermediate harvest planning in Turkish forestry. The present study applied a GP model in final yield harvest planning, which is the other main aspect of forest management plans. This study can also be improved by integrating these different models into one comprehensive model to address both problems at the same time.
The biggest disadvantage of this study is that it does not take into account natural disturbances such as forest fires, insect and disease outbreaks, and drought and windthrow. This is not unusual, since most forest planning models ignore these issues, due to their high level of uncertainty. However, the process described has some major advantages over the current approaches to develop forest management plans in Turkey. Since the regeneration operations in Turkey are very costly and locating experienced forestry workers to employ to conduct harvesting and regeneration activities is not easy, using this type of model will help forest enterprises to regenerate their forests successfully. Some certainty and regularity of annual production levels can alleviate wood flow fluctuations and help forest enterprises increase their forestry operation efficiency. Further, they will be better positioned to meet the needs of local markets in a sustainable manner by regulating final yield harvest, a major source of income. Should decision-makers consider that this study be useful for forest planning and contribute to Turkish forestry, it would be of great benefit to develop a special matrix generator to solve these (and similar) problems. Finally, the approach can be tested in different planning units and can be employed world-wide in the planning of even-aged forests, primarily pine forests.


Utilization of Technology for Early Warning of Natural Disasters in Indonesia

This research aims to determine the signs of a natural disaster that may occur in Indonesia. Indonesia is a country that is very prone to natural disasters, for example, volcanoes and tsunamis, which caused by earthquakes under the sea. This research used a qualitative research method. The respondent involved in this research is several government agencies that are authorized for natural disasters in Indonesia. The results of this study show a high level of knowledge of early warning of natural disasters, especially in prone areas to natural disasters to reduce casualties when natural disasters occur. The comparison when an area does not have an early warning system to know the good and bad impacts. This study concludes that many people do not understand the code given when the natural disaster early warning device sounds. Besides, a lot of them do not understand the signs intended in natural disaster detection devices. Therefore, there is a need for socialization from the government so that people understand the warning technology early.

Introduction
The use of information technology has now become a new phenomenon in society. People began to be served with technology in all aspects of their lives, one of which was the use of technology for an early warning of natural disasters in Indonesia. Even though natural disasters are difficult to predict, information technology as an early warning tool for natural disasters can help humans in reducing losses or casualties, so that people can be evacuated long before natural disasters occur. Nowadays, information technology has changed the face of the world from the real world to the virtual world. The world that we see nowadays is a world that has undergone an IT revolution that is the transformation from the existence of boundaries into infinity [1]. Therefore, the cost of implementation should not be a barrier to the development of an early warning system for natural disasters because of the benefits we can feel. The development of information systems and information technology in an organization and its use are needed to continue to improve competitiveness in various fields [2]. This causes system changes in the company that can change their performance. This means that early warning systems for natural disasters will affect human life because they utilize technology. Earthquake Early-Warning (EEW) is the delivery of ground shaking alerts or warnings. It is distinguished from earthquake prediction in that the earthquake has nucleated to provide detectable ground motion when an EEW is issued [3]. Explosive volcanic eruptions can eject large amounts of ash into the atmosphere, posing a serious threat to populations living near the volcano. The abrupt occurrence of such events requires a rapid response and proper volcanic hazard evaluation [4]. In Indonesia, the conveyance of data identified with early notice of catastrophic events has utilized internet-based life, for instance, Twitter. In any event, as indicated by one examination referenced that this exploration reasons that the Twitter-based admonition framework showed its incentive as a feasible supplement to Indonesia's InaTEWS -an extensive debacle data the board framework for governments -by advising people in general and making open an incentive through IOP Conf. Series: Materials Science and Engineering 879 (2020) 012011 IOP Publishing doi:10.1088/1757-899X/879/1/012011 2 its correspondence speed, reach, and data quality [5]. Currently, the beginning implementation of regional tsunami warning infrastructures indicates a new phase in the development of TWS. A new generation of TWS should not only be able to realize multi-sensor monitoring for tsunami detection [6]. Historically, empirical research has focused on the individual components or sub-systems of EWs, such as hazard monitoring, risk assessment, forecasting tools, and warning dissemination [7]. Seismic tremor early cautioning frameworks can be a helpful apparatus for lessening quake perils if urban communities are well situated as for quake sources and their residents are appropriately prepared to the reaction to seismic tremor notice messages [8]. The basic fundamental is that every EWs should be seen as a social procedure that frequently includes specialized segments inserted in their social setting [9]. The December 2004 tsunami showed the requirement for a successful wave early cautioning framework for the Indian Ocean [10].
The purpose of this research is to know about the benefits of early warning of natural disasters in Indonesia from various aspects. Therefore, people can understand more in all aspects of the natural disaster early warning system. Moreover, we discussed the benefits of an early warning system for natural disasters so that this system can be felt by the wider community. This research used a qualitative approach method.

Method
This research used a qualitative approach combined with content analysis of several examples of journalism works related to the development of technology about disaster early warning in Indonesia. Besides, this research also used interview methods with several informants from government agencies that are authorized with all matters relating to natural disasters. Several data sources also include a variety of literature reviews from various sources about the benefits of using technology in early warning of natural disasters in Indonesia.

Results and Discussion
The use of technology for early warning systems in Indonesia has begun to be widely used since the natural disaster of the Tsunami that struck Aceh in 2004. Learning from this experience the importance of an early warning system is made to reduce the number of victims when a natural disaster occurs. Besides, the natural disaster early warning system technology is also used for volcanic eruptions by using a hot cloud temperature sensor. When the temperature of the hot clouds rises, the status of an active mountain can be said to erupt. The use of the natural disaster early warning system in its development currently uses several advanced features, for example, at this time the natural disaster early warning system has been designed which reports its reports directly to this smartphone can be more effective and accelerate the dissemination of information. The following is an early warning system that has begun to be used in Indonesia.

Tsunami Early Warning System
The Tsunami Early Warning System in Indonesia was built after the tsunami in Aceh in 2004. Therefore, the tsunami early warning system is a mature system in its development in Indonesia. The Indonesia Tsunami Early Warning System, or what we might call InaTEWS, predicts tsunamis when there are earthquakes under the sea or landslides under the sea that cause shifts in the earth's layers that triggering sea waves to the coast. The system that works in InaTEWS includes various aspects of the indicators that cause tsunamis so that when a tsunami is predicted to come, sensors installed at sea will provide information to InaTEWS management centers about the phenomena recorded by sensors (see Figure 1).  Figure 1 shows that we can find various information that occurs in the middle of the ocean when there is movement in the earth slab or changes in sea water waves. The information conveyed in this system can already be accessed openly. Therefore, we can find out for ourselves if a natural disaster phenomenon occurs. In this system, we can also see an estimated map of the impact when the tsunami comes, so that we can prepare for the evacuation process on the coast. This system is also equipped with information when an earthquake occurs at the bottom of the sea, the information consists of the epicenter, the distance from the coast to what level of potential tsunami will occur (see Figure 2).

Mount Eruption Early Warning System
In the past, residents who lived around the slopes of the volcano only relied on a caretaker to monitor volcanic activity and also analyze animal characteristics to find out whether the mountain's activity was increasing or not. However, this method is considered ineffective because if the caretaker miscalculates it will be fatal. The Early Warning System for volcanic eruptions uses sensor technology on seismometers and tiltmeters around the foot of the active mountain. This sensor will give a signal to if there is recorded volcanic activity, then translate the warning in the form of a warning with the help of speakers mounted in the center of residential areas. Its function is so that the public knows about the status of the volcano activity. Signals sent by these sensors are also received by the volcanic watchdog. Later the signal is translated back adjusted to the status of volcanic movements that occur within the volcano (see Figure 3).  Figure 3 above, it can be seen sensors that are used to detect volcanic activity of volcanoes installed in a place that is used as a benchmark for calculating activity. The device, which consists of a chipset, radio unit, and tiltmeter, is used not only in one place. However, in several places from the foot of the mountain to the highlands where there is an estimated lava path if a volcano erupts. In addition to censoring the movement of volcanic activity, there are also speakers installed around the settlements that function as providing information to the public to be prepared to be evacuated in case of danger in volcanic activity on the volcano (see Figure 4).

Figure 4. Speaker that serves to provide information
Waves received by sensors of volcanic activity, translated and conveyed to the public through sound waves that are understood by the community as a sign or status of a volcano. The technology used in the Mount Explosion Early Warning System has been very good and helps us in detecting volcanic activity that is happening. One of the benefits of this system is that it reduces the victims of a volcanic eruption, so that the evacuation process can be faster even before the volcano erupts. It is just that in the process, the government has not fully provided this technology in every active volcano in Indonesia. Therefore, there is a need for new innovations so that the development of an early warning system for natural disasters can be widely felt.

Earthquake Early Warning System
For the early warning system in Indonesia, it is still new. In this case, the BMKG, which has the authority over natural disasters in Indonesia only tested this system in 2019. This system is used to detect tectonic activity that occurs in Indonesia. This system is called InaEEWS (Indonesia Earthquake Early Warning System) the results of the BMKG research are the achievements of a long-standing research together with the Institute of Care Life from the People's Republic of China. The results of the collaboration between the two countries resulted in an earthquake disaster early warning system technology. This system works by calculating the arrival time of the S wave (shear) which has the potential to produce waves (preasure) to provide a warning signal. The sensor in this system is installed in several places that are believed to be the epicenter or moving plate (see Figure 5).

Figure 5. InaEEWS work process
From the picture above we can see the process that occurred when the early warning system was running. This system can later reduce the adverse effects when an earthquake occurs. It is because we can predict the event. Besides, we also need to know that this system is only a tool to predict, not to reject the coming of a disaster. Therefore, we still need to know what we have to do when an earthquake occurs.

Early Warning System in Indonesia
The government, with the ability of the experts in it, can certainly determine its own technology and disaster early warning system that is most appropriate. A good system definitely requires reliable infrastructure. However, clearly, infrastructure is only one part of the system.That is why, reflecting on the experience of the tsunami that we have experienced, the early warning system should also include strict and fast decisions and coordination between relevant agencies at all levels of the process; sectoral egos can no longer be tolerated. Even more important, the early warning system must really be felt by community groups that are potentially affected by disasters. Early warning is not enough to only be accessed by the public; there must be a mechanism that ensures that early warning information really reaches potential community groups affected by disasters. The development of an early disaster information system that we need may require a very large budget. The issue of the budget should not dampen our sincerity to build it as an investment in the security and safety of all citizensHowever, we cannot bargain for the position of this country which is mostly located in the Pacific Ring of Fire, has various volcanoes, even active volcanoes in the middle of the sea. Likewise, we cannot negotiate the need for an adequate disaster early warning system as part of disaster adaptation and mitigation efforts.

Conclusion
The use of technology in the natural disaster early warning system in Indonesia has begun to function in several locations that are considered prone to natural disasters. From the description above, we can draw the conclusion that each use of the Early Warning System has many benefits so that its development must continue to be updated. In Indonesia, the use of the Early Warning System still needs a lot of improvement, especially in terms of tool maintenance. It is because there are a lot of theft of sensors or other devices that support this system where people are not responsible for installing it. The government must have standard rules regarding all uses of this system so that the technical can be more effective and the benefits can be greater.