Methodological support for increasing the technological potential in industry according to technological potential level and cyclical development

The article discusses the issues of increasing the level of technological potential on the example of the Russian economy in the form of targeted state intervention during the implementation of import substitution. In the paper, the increase in technological capabilities is investigated taking into account the life cycle of the industry. The practical application of methods for assessing the level and vector of technological potential, as well as a dynamic model of organizational decision-making, is displayed.

Introduction
The study of the process of development of the technological potential of the industrial sector made it possible to establish its cyclical nature, due to the emergence of tendencies to decline (decrease in the level of technological potential) and to growth (its increase). In this context, the increment is carried out in leaps and bounds due to the need to increase competitiveness and reduce the technological lag of industry, which falls on the time interval between the economic crisis and the revival of the economy, which is taken in work as a "bifurcation point". The bifurcation point is the moment in time at which a managerial decision is made to neutralize the intra-system crisis and determines further development, in particular through the introduction of comprehensive protectionist measures and import substitution.
The main conditions determining the emergence of the bifurcation point, as well as the factors necessary for the increment of the technological potential of industries ( Figure 1) were analyzed and systematized.

Materials and methods
One of the factors in overcoming the negative dynamics in the development of technological potential on the example of the branches of the industrial sector of the Russian economy under the influence of foreign economic and foreign policy processes was the state industrial policy in terms of targeted impact by introducing sectoral import substitution plans in 2014. The results of the analysis of time series reflect the change in the parameters of the development of capital-forming industries in the process of impulse impact on the technological potential of the industrial sector and make it possible to detect the relationship between the volume of imports, the 2 volume of production and the level of utilization of production capacities, and the beginning of the implementation of the state impulse impact (for example, sectoral import substitution plans).
Analysis of the main economic indicators of the high-tech industry [3] allows us to conclude that the main factor in the controlled withdrawal of the industry to the growth phase through increasing the technological potential is a purposeful state industrial policy. At the same time, the basis for technological re-equipment is methodological support for assessing the level of technological potential, the vector of its development, and the model of dynamic analysis for the formation of basic organizational and economic decisions.
The formation of technological potential consists in the identification, structuring, and connection of resources to ensure the output of industrial products. In the process of capacity-building, the quantity and quality of resources are accounted for, technological limitations are determined, a problem field is built, and the possibility of increasing the technological potential of the industrial sector of the economy is evaluated.
Technological capacity-building of industries is achieved by impulse effects on an acceptable amount of technological capacity increment on an extensive or intensive basis. An extensive increase in technological potential consists in attracting additional resources without changing their qualitative properties, while intensive build-up implies updating the main production assets with advanced and productive technologies, the use of more qualified labor. The practical implementation of intensive technological capacity building is carried out through the activation of dynamic abilities.
Dynamic abilities are the ability to reconfigure resources, which ensures the continuous updating of technologies, by activating either absorbing abilities (in the implementation of borrowing and transfer strategies) or the ability to produce technological innovations (in the implementation of strategies for building up and innovation-distribution differentiation), through integration, cooperation and network interaction of economic entities.

The power consumption analysis
One of the main conditions for the implementation of the first strategy is the availability of a sufficient level of technological, scientific, and technical potentials [4][5][6][7][8][9][10][11][12][13][14]. At the same time, the decline in the quality, consumer, and functional characteristics of industrial goods complicate the continuous updating of technologies due to the growth strategy and form the need to focus on the strategy of innovation-distribution differentiation based on the stratification of industries in the industrial sector, taking into account the level of technological potential and the level of capacity utilization [15], which 3 act as objective indicators for assessing production capabilities, branches of the industrial sector of the economy.
The increased technological potential ensures the production of competitive industrial products in the process of its use [16,17]. To determine the parametric characteristics of the processes of development of technological potential, the method of econophysics (the method of analogy in the model of mechanical movement of the body) was applied, which makes it possible to consider the branch of the industrial sector as a certain object with a certain mass, speed, and position in the process of technological development. The state and dynamics of the physical body are completely characterized by a combination of kinetic and potential energies. Similarly, the dynamics of the industry can be fully characterized by kinetic and potential capacity, which are analogies of physical quantities and will fully characterize the processes of building and using the technological potential of industries [18][19][20][21]. At the same time, the volume of industrial output, in particular the volume of innovative products produced, can act as an analogy for the speed of the physical body. As an analogy to the position of the physical body, the level of the technological potential of the industrial sector can act [2,22]. Thus, such a representation of the dynamics of industries will allow analyzing the processes of development of the technological potential of the industrial sector in a two-dimensional space. The indicator of the development of the technological potential of the industrial sector will be its vector I, which is the sum of the vector of increasing the technological potential and the vector of its use, characterized by length and direction. The effectiveness of the development of the technological potential of the industrial sector implies the presence of a vector direction in which the processes of building and using this potential are balanced I0.
Deviation of the vector of development of the technological potential of the industrial sector from I 0 -will mean the intensification of either the process of increasing(I4),or the process of using the technological potential of the industrial sector (I1), and negative (I2, I3). The collinearity (codirectionality) of the vector of development of the technological potential and the vector I0 will indicate the balance of the processes of increasing and its use (I5, I6).
Determining the vector of development of the technological potential of the branches of the industrial sector makes it possible to use it as a tool for managing the processes of building and using the technological potential of the industry.
At the same time, the formation of organizational solutions is carried out for stratified groups of industries in the industrial sector that have similar factors for the development of technological potential. This approach makes it possible to build promising trajectories for the development of technological potential, taking into account the identified trends. Industries with the greatest technological potential and available opportunities for reloading production capacities should be considered as locomotive industries or growth points, while industries with reduced technological potential should be focused on meeting the needs of the locomotive industries and subjected to restructuring if necessary Figure 1 shows that the most energy-intensive equipment of the plant are electrolyzers, welding machines, and pumps. They account for about 47% of the total power consumption of the enterprise.

Conclusion
The analysis showed that not a single branch of the industrial sector of the economy fell into the I-III sectors characterized by uncompetitive development. Branches of the industrial sector of the economy located in these sectors require the most stringent direct and indirect measures of a state policy of a stimulating nature (restructuring and re-profiling of enterprises) that contribute to the transfer of industries from the crisis phase.
Sectors VI of the VI sector have a negative vector of development of technological potential and its value below the average, while building up technological potential is limited by high utilization of production capacities. In this case, organizational and economic instruments of industrial policy should stimulate the inflow of real investment, including by attracting foreign investment.
Despite the fact that the branches of the IV and V sectors have sufficient reserves for increasing the volume of industrial production, the increase in technological potential is carried out not on the basis of technological modernization, but at the expense of highly profitable investments. Relatively short payback periods of financial resources determine the investment attractiveness of these industries. In view of the stability of demand for products, there is no need for significant technological capacity-building. Organizational and economic instruments of industrial policy, in this case, can be concentrated mainly on the renewal of fixed production assets, since, as a rule, in this group of industries, there is a high level of qualification of personnel. Technological modernization of fixed production assets will ensure uniform dynamics of changes in technological potential, improving the quality of products and its competitiveness in the domestic and foreign markets.
Sector IX sectors have a more stable pace of building technological potential. Indicators of the use of fixed production assets indicate the presence of a process of technological modernization, respectively, industrial policy measures to build technological potential can be aimed at ensuring the consolidation of the achieved positions and maintaining the formed level of technological potential, by improving the quality of use labor resources and preservation of export indicators.
In the VIII sector, there are industries for the production of vehicles and equipment and chemical production. In relation to these groups of industries, organizational and economic instruments of industrial policy should be aimed at ensuring a 5% growth rate of the enlarged groups of indicators of technological potential, which can be achieved by activating dynamic abilities (increasing the number of own developments, localizing design and engineering centers, increasing the share of innovation costs in the cost structure of production). Increasing the technological potential of industries located in the VIII and VII sectors can become a priority direction of the state industrial policy in the context of import substitution and the transition to a strategy of innovation and distribution differentiation.


Law Enforcement of Criminal Acts of Terrorism Through a Humanist Approach based on Pancasila Law

— Pancasila, as the source of all sources of state law, is the result of deep exploration, analysis, and reflection by the founding national figures of the Republic of Indonesia. All aspects and values of civilization, history, geography, demography, religion, ethnicity, culture, customs are united in five basic values (grand norm), and each value which is one with other values is an inseparable unity. Terrorism and radicalism are real threats to the existence of Pancasila as an ideology. It takes consistency, awareness, and strong determination from all elements and components of the Nation to replace the ideals of Pancasila law in building every order of national and state life through statutory regulations because Pancasila is the best vaccine in building immunity/deterrence of the Indonesian people from individuals. Radicalism and terrorism and other ideologies. The Pancasila philosophy must continue to be echoed and transmitted in every statutory regulation made, starting from the highest to the lowest provisions, and implemented in the attitudes and legal behavior of the community. This paper was written using the statute approach, the case approach, the historical approach, and the conceptual approach. After the materials have been collected, a study or analysis is carried out using a qualitative prescriptive and normative approach according to the legal issues raised. The purpose of this paper is to present a comprehensive scientific study on the threat of radicalism and terrorism to the existence of Pancasila as the State Ideology and legal philosophy conceptions, placing Pancasila as the source of all sources of state law as reflected in every existing statutory regulation and legal practice to cut the eyes of radicalism and terrorism in Indonesia.

I. INTRODUCTION
Pancasila is the source of all sources of state law. Pancasila as the ideology and basis of the state, the view of life, and the personality of the Indonesian nation. These conceptions are the philosophical foundation as well as the practical basis for all the people and the Indonesian nation in building a social order and legal ideals to realize the goals of the State [1]. In compiling and controlling the system needed by the state to organize, regulate and organize the survival of the community, regulation or legal order is needed so that every element and existing social institutions can be managed harmoniously. For a country [2].
Terrorism and radicalism are a form of threat to defense and security that are very serious and dangerous for the survival of the nation and state because the threat is aimed directly at the existence of Pancasila as the State Ideology and as the source of all sources of law applicable in the Unitary State of the Republic of Indonesia. From a legal perspective, terrorism and radicalism are inseparable. Radicalism is the fuel (source of energy) that moves anti-Pancasila groups to undermine state sovereignty in the form of terrorism crimes with the ultimate goal of replacing the Pancasila ideology with theirs, and that means destroying the Unitary State of the Republic of Indonesia and replacing the government or regime in power using other methods. Unconstitutional [3]. radical idealism. with the belief to fight for religion and justice [5].
According to Hoffman as quoted by Petrus R. Golose, identifying at least 5 (five) motives for terrorism, namely: 1) Nationalist-Separatist; 2) Religious Ideology; 3) The main issue (single issue); 4) Sponsor country; and; 5) mental disorders (mental disorders). There are examples of terrorism with religious motives such as hard-line Islam such as al-Qaeda, al-Jama'ah al-Islamiyah (JI), Islamic State of Iraq and Syria (ISIS), or hard-line Hinduism such as the Tamil Tigers in Sri Lanka and Sikhs in India.
From the various terrorist attacks that have occurred in Indonesia since before and after the enactment of Law Number [7].
In the early days of independence or the Old Order era, the handling of terrorism was carried out by prioritizing a militaristic approach, then in the New Order era, the handling of terrorism prioritized the Intelligence approach. After the Reformation era, the handling of terrorism used a law enforcement approach. This approach is also in line with UN policy as stated in the Convention for the Prevention and Punishment of Terrorism, November 1937, terrorism is an international crime that can be prosecuted and punished under national law that has jurisdiction over the crime.
The crime of terrorism in Indonesia, which tends for religious motives, has its challenges in dealing with it, including: (1) efforts to eradicate it are dealing with belief as an ideology; (2) the spread of radicalism is systematic and organized in society; (3) the organization can metamorphose; and (4) the handling of terrorism crimes which only focuses on criminal penalties.
Various approaches have been taken in preventing and tackling terrorism crimes by involving various cross-sectoral stakeholders. The causal factors of criminal acts of terrorism are complex, so the approach used is adjusted based on the motives of each person involved in the crime [8]. If the motive for the crime of terrorism is religious reasons, then the law enforcement approach alone is certainly less effective in reducing the number of terrorism crimes. Likewise, if the motive for terrorism is carried out based on reasons of social and economic injustice, the approach that must be taken is a special treatment in the social and economic fields to create a balanced sense of justice. To provide a more solid legal basis to ensure legal protection and certainty in the prevention and eradication of criminal acts of terrorism, as well as to meet the legal needs and developments of the community, it is necessary to make changes proportionally while maintaining a balance between the needs of law enforcement, protection of human rights, and socio-political conditions in Indonesia [9].
From the general explanation, there is an emphasis on the paradigm in law enforcement for criminal acts of terrorism, namely the balance between prevention (preventive) and eradication (repressive). Equally important is the non-penal approach related to the fundamental aspects of radical beliefs and anti-Pancasila ideology from the perpetrators and their families and group members as supporters of acts of terrorism. Because of the criminal justice system (CJS), sentencing is neither the end goal nor the only way to achieve the criminal goals or objectives of the criminal justice system.
Observing legal and socio-political developments during today's society which is dominated by issues of identity politics, intolerance, and radicalism, so this research has a very strategic urgency, both from a legal perspective and from a national perspective in the form of four pillars, namely: 1) Pancasila; 2) the 1945 Constitution of the Republic of Indonesia (UUD 1945); 3) Homeland; and 4) Bhineka Tunggal Ika, with the main issue being "The Legal Conception of Pancasila in Combating Terrorism and Radicalism in Indonesia". There are 2 (two) issues to be discussed,

Advances in Social Science, Education and Humanities Research, volume 628
namely: 1) how is the reflection of Pancasila as the philosophical foundation in various laws and regulations for countering terrorism and radicalism in Indonesia;

A. Crime of Terrorism
Terror and terrorism are two similar words that have recently become popular topics. The term terrorism itself is related to the words terror and terrorist, which generally do not have a standard and universal definition or definition [10]. However, international countries agree that the term has a negative connotation that is equivalent to or equivalent to the term "genocide". Terror is a phenomenon that has quite a long life in history, this is evidenced by the root of the word terror itself, namely the phrase "cimbricus terror". The Romanesque phrase means "to frighten" which describes the panic that occurs when opposing soldiers act fiercely and violently.
Then this word developed for the first time during the French Revolution to become le terreur or terrere which was used when there was brutal violence by beheading many people accused of anti-government activities so that terrorism could be interpreted as liking to intimidate and brutalize civilians with certain reasons. The meaning of terrorism then experienced a shift from what was originally an act committed by an authoritarian ruler with political reasons into the category of crime against state and crime against humanity which resulted in victims of the community of a government so that the political and religious ideals of the terrorists were achieved [11].
In the dictionary of International Relations, it is stated that terrorism is the activity of state or non-state actors who use violent techniques in their efforts to achieve political goals. In line with this understanding, A. Hasnan Habib66 defines terrorism [12]. Terrorism is the use of threats of physical violence that are planned, prepared, and carried out suddenly against direct targets, which are usually non-combatants to achieve a goal. Terrorism involves groups seeking to overthrow certain regimes, correct group or national grievances, or undermine the existing international political order.
Terrorism itself is an act that aims to provide excessive fear through threats or other forms of violence so that not only the injured victims are caused by it, but also the loss of life so that it is evidence of the pressure carried out by groups using terrorist methods [13]. Terror and terrorism are two similar words that have recently become popular topics. The term terrorism itself is related to the words terror and terrorist, which generally do not have a standard and universal definition or definition. According to the national laws of each country, this does not mean eliminating the evil nature of the act and thus means that the perpetrators of terrorism are free from prosecution [14].
Nullum crimen sine poena, the sound of an old legal principle, which means that no crime should be allowed to go unpunished, but the fact that terrorism is an Internationally Organized Crime makes it very difficult to eradicate this type of crime without the cooperation and common understanding among the state country [15].

B. Law Enforcement Concept
Law enforcement is the process of making efforts to enforce or function legal norms as guidelines for behavior in traffic or legal relations in social and state life. What people mean by law enforcement so far seems to only focus on the repressive actions of law enforcement officers in carrying out firm reactions to the prosecution of criminals [16].
The meaning of law enforcement in this way is very narrow because the authority of law enforcement only seems to be the responsibility of law enforcement alone. Law enforcement in a broad context is in the realm of real or factual actions, actions, or behaviors that are following binding rules or norms. However, to maintain and restore order in social life, the government is the security actor.
Viewed from the point of view of the subject, law enforcement can be carried out by a broad subject and can also be interpreted as an effort to enforce the law by the subject in a limited or narrow sense. In a broad sense, the law enforcement process involves all legal subjects in every legal relationship [17]. Anyone who applies normative rules or does something or does not do something based on the norms of the applicable law means that he is carrying out or enforcing the rule of law. In a narrow sense, in terms of the subject, law enforcement is only defined as the efforts of certain law enforcement officials to guarantee and ensure that a rule of law runs as it should. In ensuring the enforcement of the law, if necessary, law enforcement officials are allowed to use force.
In a broad sense, law enforcement also includes the values of justice contained in the sound of formal rules as well as the values of justice that live in society. However, in a narrow sense, law enforcement only concerns the enforcement of formal and written regulations. Therefore, the translation of the word 'law enforcement' into Indonesian uses the word 'law enforcement in a broad sense, and the term 'rule enforcement can also be used in a narrow sense.

III. RESEARCH METHODS
Research has another term or known as research. The research comes from English, namely research which comes from the word re (re) search (search) this research which has the term research can be interpreted as searching again. This research activity is based on the curiosity of someone who is then referred Advances in Social Science, Education and Humanities Research, volume 628 to as a researcher in carrying out research activities.
Research is a form of expression of curiosity carried out in the form of scientific research activities. This research is carried out with a sense of trust in the object being researched by finding out the causes and effects that arise or occur in the object of research.
This study uses a normative research method. According to Peter Mahmud Marzuki [14], normative legal research is a process to find a rule of law, legal principles, and legal doctrines to answer the legal issues faced. In this type of legal research, the law is often conceptualized as what is written in legislation or the law is conceptualized as a rule or norm which is a benchmark for human behavior that is considered appropriate.
Based on the problem approach and the required data sources, the data collection in this study was carried out through a literature study by reading, quoting, taking notes, and identifying data that was following the problem, and collaborating with the data of applicable laws and regulations. After collecting data, then processing the data obtained is used to analyze the problems studied. Data processing in this study was carried out by Data examination, classification, and data compilation.

A. Pancasila as the Philosophical Foundation in various laws and regulations in countering Terrorism and Radicalism
Pancasila as the view and personality of life in the nation and state in the last two decades has experienced a very sharp degradation, this is due to several aspects, such as 1) the existence of a reformation euphoria which considers all the life structures of the nation and state that were built during the New Order government did not exist. need to maintain again; 2) the flow of cultural globalization due to advances in communication and information technology, has removed territorial barriers from one country to another (borderless), causing the life of the nation and state to be very liberal; 3) the existence of fundamentalist groups who want degradation and discredit the ideology of Pancasila and its government system as the cause of the state's failure to realize its goals [18].
The phenomenon of degradation of the practice of Pancasila values and the application of values that highlight identity politics does not only appear in the interaction of social relations and public policies but has infiltrated the legal political order in various laws and regulations. From a legal perspective, the phenomenon that occurs is certainly very dangerous because it has deviated far from the conception of the basic principles of universal law and the ideals of Pancasila Law [19].
The objectives of the Unitary State of the Republic of Indonesia are [20]: 1) to protect the entire Indonesian nation and the entire homeland of Indonesia; 2) promote the general welfare; 3) educate the nation's life; 4) implement world order based on: (a) independence, (b) eternal peace, and (c) social justice. According to Franz Magnis Suseno, as quoted by Agus Riwanto, the conception of the state's moralpolitical goals requires a state to run by using the law as a guideline because it will have several ethical arguments, namely: 1) for the sake of legal certainty; 2) demands equal treatment; 3) the legitimacy of democracy, and 4) the demands of reason.
The counter-terrorism and radicalism approach as the strategy and policies carried out by the National Counterterrorism Agency (BNPT) is a hard approach through repressive measures in the form of law enforcement and a soft approach through prevention (preventive) in the form of national preparedness programs, deradicalization and counter-radicalization [21]. The general principles as a strategic framework used by BNPT in countering terrorists include: 1) The rule of law; 2) Discrimination; 3) Independence; 4) Coordination; 5) Participation; and 6) Democracy.
In the General Elucidation of Law Number 5 of 2018, that to ensure legal protection and certainty in: 1) prevention and 2) eradication of criminal acts of terrorism; 4) meet the needs and legal developments of the community; 5) changes are made proportionally, with; 6) maintain a balance between the need for law enforcement, protection of human rights; and socio-political conditions in Indonesia [22]. Prevention in the form of deradicalization is a planned, integrated, systematic, continuous process that is carried out to eliminate or reduce and reverse the understanding of radicals and terrorism that has occurred.
There are various types and hierarchies of laws and regulations, ranging from MPR Decrees, Laws/Perppu, PP, Presidential Regulations, Provincial Regulations, to Regency/City Regional Regulations. The distribution is an indicator of the complexity of the threat of radicalism and the impact that acts of terrorism can have on the joints of people's lives and state sovereignty.
The reflection of Pancasila in various laws and regulations in countering terrorism and radicalism, in the perspective of law enforcement includes a legal approach with punishment (penal) and a legal approach without punishment (non-penal) [23]. The legal principles or principles that form the basis for sentencing (penal), are conceptually contained in the articles of the Criminal Code and the Criminal Procedure Code, including the principle of legality (nullum delictum nulla poena sine praevia lege), the

Advances in Social Science, Education and Humanities Research, volume 628
principle of equality before the law (equality before the law). ), the universal principle, the territorial principle, the personality principle, the protection principle, the principle of open court justice, the opportunity principle, the presumption of innocence and presumption of guilt, for the sake of justice and based on the Almighty God (pro justitia), and some other legal principles or principles.
All of the principles or legal principles adopted when examined through the values contained in Pancasila are essentially a reflection of the values from the first precepts to the fifth precepts, where this can be tested using empirically comprehensive steps, systematic, rational-objective, analytical, and verification [24].
In article 15 of Law Number 12 of 2011 concerning the Establishment of Legislations, it is explained that following the hierarchy, laws, and regulations that contain criminal provisions (penal) in the form of imprisonment or fines are only regulated in the form of Laws, Provincial Regulations and Regency Regulations. It was further emphasized that every material content in the formation of laws and regulations must be based on principles; 1) shelter; 2) humanity; 3) nationality; 4) kinship; 5) archipelago; 6) Bhinneka Tunggal Ika; 7) justice; 8) equal position in law and government; 9) order and legal certainty; and 10) balance, harmony, and harmony. All of these principles textually reflect the values of Pancasila, Explained that the recognition and protection of human rights or citizens in the Preamble and Body of the 1945 Constitution shows that the Indonesian state is a democratic legal state because socio-legally and socio-culturally the existence of a constitution is a consequence of implementing the principles of the rule of law and the principles of a democratic state.
One of the non-penal countermeasures for terrorism and radicalism is a preventive approach in the form of activities such as 1) improving the social and economic conditions of the community; 2) increasing awareness of the law and community discipline, and 3) improving moral education. The purpose of preventive efforts is to motivate, guide, and direct the public about the dangers of terrorism and radicalism so that people are aware of the consequences of criminal penalties and social punishments that will be received if they commit a crime of terrorism. Another objective of preventive efforts is to make the public aware to protect themselves from co-optation of radical groups and terrorism.
Therefore, the non-penal legal approach in countering terrorism and radicalism is essentially a Pancasilaist legal approach that puts forward preventive measures based on the principles of humanity, expediency, protection, openness, kinship, harmony, non-discrimination, justice, consultation. , unity, nationality, and the embodiment of nature as creatures created by God Almighty.
The substance of the non-penal law enforcement approach is unknown in Law Number 5 of 2018 concerning Amendments to Law Number 15 of 2003 concerning the Stipulation of Perppu Number 1 of 2002 concerning the Eradication of Criminal Acts of Terrorism into Law, as well as with the conception of diversion and restorative justice in the system. Juvenile Justice has not reached the efforts of nonpenal law enforcement approaches for terrorism crimes committed by children. Currently, non-penal law enforcement in the prevention of criminal acts of terrorism and eradication of radicalism, in practice, is carried out before the perpetrators enter the criminal justice system or through a preventive and preemptive approach to intelligence operations.

B. The concept of Pancasila law that can break the chain of terrorism and radicalism
The forms of the concept of prevention in countering terrorism include [25]: 1) national preparedness; 2) deradicalization, and 3) counterradicalization. BNPT as the leading sector, designed deradicalization as a form of prevention through four approaches, namely: first, re-education; second rehabilitation; third resocialization; fourth reintegration. The four approaches are actualized in the form of a humanitarian approach, nationalism, economic empowerment, education, and psychological counseling for the perpetrators and their families. Counter-narrative and counterpropaganda programs through social media are some other forms of counter-radicalism that are carried out in breaking the chain of radicalism.
Deradicalization refers to the US sense. Hikam has two meanings, namely disconnecting or breaking away (disengagement) and deideologizing (deideologization). Disengagement is an effort that leads to behavior change, for example getting out of the attachment of a terrorist network group by changing behavior and life habits, to leaving all the rules of a terrorist group. Furthermore, deideology was continued by eliminating the ideological understanding of the political doctrine of radical Islam, becoming an Islam that upholds the noble values of spreading peace. Deradicalization orientation is changing a person's spectrum from radical to moderate.
In the perspective of Pancasila law, deradicalization efforts can be implemented through the values of Pancasila which are realized in a bottom-up manner by exploring and re-elevating the Advances in Social Science, Education and Humanities Research, volume 628 conception of local wisdom that is in line with Pancasila (inductive deradicalization), while topdown is done by positioning the state as the leading sector-main) to socialize the values of Pancasila through statutory instruments that can bind and create an equitable distribution of community welfare, as well as motivate every citizen to maintain and foster an attitude of harmony, tolerance, and cooperation in the process of achieving national goals (deductive deradicalization) [26].
The synergy of the two methods is expected to increase the resilience of the Pancasila ideology in the face of global ideological threats in the form of issues that provoke terrorism and radicalism activities, this method will also encourage the state and society to be able to prevent acts of radicalism through the implementation of Pancasila values transformative deradicalization) [27].
Conceptions carried out through economic, ideological, and other firms tend to be temporary diversions. The form of conception of the national approach and nationalism of Pancasila values is much more effective which is manifested in positive activities at the behavioral level. The integration of the implementation of Pancasila values into the deradicalization method is very important because it will touch the level of values, norms, and behavior of individuals which will then provide a multiplier effect in the form of creating a tolerant society, mutual respect, mutual assistance, and cooperation.
The concept of transformative deradicalization with the implementation of Pancasila values will create output in the form of people who know their identity and character as the Indonesian nation [28]. The ideology of Pancasila will be firmly practiced as a living ideology and working ideology so that people are more rational in facing all challenges of social change and the development of advances in communication and information technology. The radical ideology that occurs today creates hopelessness and irrationality for society in every social change and globalization. The very tight competition between individuals, groups and a very destructive environment creates an unhealthy and unbalanced competition that creates anarchist, intolerant, and radical traits that are far from the values of Pancasila.
The government must be able to provide an example through the reflection of Pancasila values in managing social, legal, economic, and behavioral orders as well as spearheading decision making. Continuing political education can form a participatory political culture of the society in building the nation and state in realizing the goals of a welfare state based on Pancasila.

V. CONCLUSION
Pancasila as the source of all sources of state law, at the philosophical level as well as at the practical level with its five precepts is an inseparable unit as a basis in realizing the ideals and goals of the state as stated in the fourth paragraph of the Preamble to the 1945 Constitution. In the perspective of countering radicalism and terrorism, Pancasila is a reflection of the conception of the basic principles of legislation related to countering terrorism and radicalism, both based on the type and hierarchy of laws and regulations from the highest, namely the 1945 Constitution, MPR Decree, UU/Perppu, PP, Perpres, provincial regulations, up to district and city regulations. The conception of the law enforcement approach used in countering terrorism and radicalism, both in the form of the conception of a repressive law enforcement approach (action) and the conception of a preventive approach (prevention) in the form of national preparedness, deradicalization, and counterradicalization. Pancasila is a comprehensive approach that can provide more substantial conceptions in countering terrorism and radicalism. It is necessary to reformulate Pancasila in the life of the nation and state due to the degradation of Pancasila values after the reform era so that the immunity of the Indonesian nation and people in the form of a national ideology that breathes Pancasila will be able to stem radicalism and other ideologies as a negative impact of the swift currents of globalization.


Measurement, Influencing Factors and Spatial Spillover Effect of China's Green Economy Development Level -- Spatial econometric analysis based on provincial panel data

Based on the concept of green development under the new development pattern, the evaluation index system of China's green economy development is constructed from three dimensions: green production, green life and green ecology. Based on the panel data of 30 provinces from 2005 to 2020, the green economy development level of each province is calculated, and its influencing factors are considered from three aspects: economic development, social capital and governance efficiency, and its spatial spillover effect is analyzed. The conclusions of this study are as follows: (1) During the study period, the development level of green economy in each province generally presents a steady upward trend, but there are significant differences among the provinces, with Xinjiang, Hainan, Tianjin and other provinces experiencing the largest increase, while Yunnan, Guangxi and Shaanxi provinces experiencing the smallest increase. (2) The development level of China's green economy is obviously centralized and distributed. The level of science and technology, the development of the private economy and the degree of opening to the outside world are the key factors to promote the high-quality development of green economy in China. The extensive utilization of resources has hindered the development of the green economy. The degree of openness is the main factor to promote the development of the green economy in this region. Technological progress and energy consumption are not conducive to the promotion of green economic development level in other regions.

Introduction
In recent years, China's economy has maintained a good development trend for a long time, but economic growth is accompanied by high pollution and high energy consumption, which breed serious resource and environmental problems. Green development is an important content of highquality regional development under the new development pattern. However, due to the different economic, institutional, social, cultural and natural resource endowments of different regions and provinces in China, how to balance regional economic development with resources and environmental protection has always been a key issue. The new development model can not only emphasize the efficiency of economic development, but also take into account the quality of its development, especially the green and sustainable economic model. Based on the traditional measurement model and the new connotation of green development under the new development model, it is necessary to explore the measurement system and its influence mechanism of green economic development. Therefore, it is of great significance to establish a perfect evaluation index system for the development of green economy, scientifically measure the development level of green economy in various provinces in China, explore the key factors that influence the development of green economy, and analyze its internal influence mechanism for promoting the sustainable development of green economy in various regions in China.
The development of green economy has become a hot topic among scholars at home and abroad. The research fields are divided into national, provincial and regional level. Xu et al. measured the development level of China's green economy and explored its influencing factors, while others have chosen Guangxi and the Yangtze River Delta urban agglomeration、Yellow River Basin、North China Plain、Beijing-tianjin-hebei region as the research object. On the research content, most scholars focus on the construction of evaluation system, the analysis of the temporal and spatial evolution trend and the influencing factors. At present, there are abundant research achievements on the construction of evaluation system. The existing studies are different in index selection and evaluation methods, and measures the green development level from different perspectives. Xu et al. have constructed a green development evaluation system from three perspectives: green production, green life and green ecology. Wang et al.、Yang et al. have used the non-oriented Super-SBM model and the non-expected output Super-SBM model to measure the green development efficiency, Ma et al. used entropy weight -Topsis method to evaluate the green development index, while others choose combination evaluation method and fixed-base range method. Based on different perspectives, the existing research have constructed various measurement models to explore the factors that affect the level of green development. Some scholars based on traditional econometric models, such as fixed effect model、Tobit model、Dynamic panel model, etc. However, most scholars pay more attention to the spatial correlation among variables and use spatial econometric models to study the spatial spillover effects of the influencing factors. From the perspective of spatial correlation, Feng et al. constructed a panel space dobbin model with two-way fixed individual time, and studies the effect of industrial synergy on urban green development, spatial spillover effect and attenuation boundary. From the perspective of spatial heterogeneity, most scholars use panel data to construct GWR model to explore the spatial differences of influencing factors.
To sum up, few scholars have studied the spatial spillover effect of the influencing factors of China's green economy development level from the perspective of spatial correlation. Therefore, based on the panel data of China (excluding Hong Kong, Macao, Taiwan and Tibet) from 2005 to 2020, this study uses FHARA algorithm to screen the evaluation indicators of green economic development level, and uses entropy weight -Topsis method to calculate the development level of green economy in various provinces of China. This paper constructs the Spatial Durbin Model, explores the spatial spillover effect of each explanatory variable, and puts forward relevant suggestions to promote the development of green economy, in order to provide effective reference for China's green coordinated development.
This research innovation mainly includes two points. Firstly, the innovation of research methods. In order to solve the problem of redundant attribute information, the FHARA algorithm is used to construct the evaluation system for green economic development index. Combining with the relevant connotation of green development under the new development pattern, the evaluation index system of green economy development level is constructed from three aspects: green production, green life and green ecology, and the development level of China's green economy is measured and analyzed. Considering its influencing factors from the perspectives of economic development, social capital and governance efficiency, this paper explores its spatial spillover effect and provides policy suggestions for the high-quality development of the green economy.

Construction of Evaluation Index System for China's Green Economy Development
Based on the concept of green development under the new development pattern and referring to the research of Xu and other scholars, the study has constructed the evaluation index system of green economy development level from three aspects of production, life and ecology, and preliminarily determines the indicators of green economy development level based on this ( Green production Sewage emission per unit GDP Sewage emission / gross regional production -Sulfur dioxide emission per unit GDP Sulfur dioxide emission/ gross regional production -Soot emission per unit GDP Soot emission/ gross regional production -The amount of agricultural pollutants used per unit cultivated land area (plastic film usage + plastic film usage + pesticide usage)/cultivated area -GDP per capita Gross Regional Production/resident population + Considering the redundancy and repetition of information in the indicators selected by manual evaluation, the redundant indicators will cause a large amount of overall information collection and complicated model calculation, which is not conducive to the follow-up research. In this study, according to the FHARA algorithm proposed by liu et al., the reduced attribute set is generated. This method uses the greedy strategy to extract the attributes that can make the positive region contain the largest number of nodes from the unselected attribute set, and add them to the reduced attribute set until the positive region can't continue to increase with the addition of a single conditional attribute.
Considering that the neighborhood radius in the FHARA algorithm can be selected artificially, the neighborhood radius ( θ ) is 0.01-0.3 during the sampling test. In this process, the number of attribute sets generated by the FHARA algorithm and the proportion of positive region in all sample spaces are recorded.  The proportion of positive area in sample space can represent the ratio of sample information contained in this attribute set under radius measurement. With the increase of radius, this constraint strength is stronger, and when the radius exceeds a certain value, the specific gravity displayed is no longer of reference significance. Considering that the attribute set generated by FHARA algorithm adopts greedy strategy, the result of the algorithm is not the optimal attribute set, so the optimal selection proportion is 95%. At this time, 0.12 θ = , a total of 12 indexes are finally selected ( Table  2).

Measurement and Analysis of China's Green Development Level
Considering the influence of dimensions, the standard treatment is adopted to unify the indicators into positive indicators. Then, the entropy weight -Topsis method is used to measure the green development level of 30 provinces and cities in China from 2005 to 2020 (Table 3). From the analysis of the data in the table, it can be concluded that from the general trend, most of the provinces have shown a steady growth, with Xinjiang, Hainan, Tianjin and Guizhou showing the largest growth, with Xinjiang showing an increase of 109.3%, while Shaanxi showing the smallest increase, with an increase of 21.3%. The overall distribution has shown a great change. The development advantages of Beijing, Tianjin and the Pearl River Delta have been gradually established, with an overall increase higher than that of other regions. The development level of green economy in China's provinces is extremely unbalanced, and targeted measures should be taken to further promote the coordinated development of green.

Spatial Weights Matrix
Considering that the connection between provinces and cities is an adjacent relationship, Queen adjacency is used to indicate the adjacent relationship between provinces and cities. Among them, Hainan Province is modified to be connected with Guangdong Province and Guangxi Province.
Queen adjacency space weight matrix is defined as follows: Note: when i=j, the weight is defined as 0.

Global Spatial Autocorrelation
Spatial autocorrelation can analyze the spatial correlation characteristics of a certain attribute, which is a measure of agglomeration degree in spatial domain. Studies have shown that economic geography has obvious polarization and diffusion effects, and the ecological environment has obvious characteristics of regional differentiation. In this paper, the Moran's I index is used to measure the spatial correlation degree of the development level of green economy in China's provinces. The calculation formula is as follows: n is the number of counted provinces， ij W is the Spatial Weights Matrix， ̅ is the average value of green economy development in 30 provinces. Moran's I index is between [-1,1]. And the index above 0, which indicates that the development level of green economy has overall spatial positive correlation in space, and that the regions with higher development level of green economy in various provinces in China are clustered in space, and vice versa. The green economic development scores of various provinces in China are imported into Geoda software to calculate the Global Moran's I index (Table 4), and a trend chart of Moran's I index from 2005 to 2020 is drawn ( Figure 3). As can be seen from Table 4, the Moran's I index from 2005 to 2020 was far greater than 0, and all of them passed the significance level test of 5%. This shows that there is a significant global spatial positive correlation between the development levels of green economy, that is, the development levels of green economy in various provinces in China are clustered.  486 At the same time, from the time dimension analysis, the Moran's I index showed volatility from 2005 to 2020, but in recent years the Moran's I index was higher and showed an upward trend, showing a strong spatial correlation. This indicates that the development level of regional green economy has been greatly affected by the surrounding areas, so we can further try to build a spatial econometric model and explore its internal influence mechanism.

Local Spatial Autocorrelation
The global spatial autocorrelation measures the spatial correlation characteristics of the whole research area, but it cannot clearly show the local spatial situation. Therefore, the local spatial autocorrelation is used to reveal the correlation features of the local space. Its essence is to decompose the global Moran's I index into each province, for spatial test individual I is: Moran scatter plot can intuitively reflect the local spatial correlation characteristics of the development level of green economy in China's provinces. It is divided into four quadrants, each corresponding to a different type of spatial aggregation. The specific explanation is shown in Table  5.  In order to more intuitively reflect the relationship between the neighboring provinces of the green economy development level, this study has drawn the Moran scatter diagram of the green economy development level. Due to the space limitation, only 2005, 2010, 2015 and 2020 are shown. Looking at the four graphs (a)(b)(c)(d) in Fig. 4, most of the counted provinces are clustered in the first and third quadrants, which indicates that there is a strong spatial positive correlation in most regions, and also indicates that the distribution of the development level of green economy shows significant spatial heterogeneity. Once again, it is proved that the spatial effect model used to study the impact of green economic development level is more in line with objective facts..

Data Sources and Variable Selection
The   Due to the limitation of data, this study initially selects 16 explanatory variables from the perspectives of economic development, social capital and governance efficiency ( Table 6): R&D expenditure intensity (x1), which is expressed by the proportion of R&D expenditure to GDP in each province; The contribution rate of high-tech industry (x2) which is expressed as the number of patents granted per 10,000 people, with reference to Li Z et al.; Private economic development level (x4) is represented by the ratio of the total number of employees in individual and private enterprises to the resident population at the end of the year, referring to Huang H et al.; The degree of opening to the outside world (x6), expressed as the proportion of foreign imports and exports of each province to the total population; Marriage and divorce rate (x7), learning from the research of Wan et al., the ratio of log of marriage registration to log of divorce registration is adopted; Total energy consumption per unit of gross industrial output value (x10), expressed as the ratio of total energy consumption (10,000 tons of standard coal) to gross industrial output value; Water consumption per unit of gross industrial output value (x11), expressed as the ratio of total water consumption to gross industrial output value; The urbanization level (x16) is expressed as the ratio of the urban population to the total population; The other explanatory variables are directly from each database. In order to eliminate the influence of dimension, standardized data is used in regression analysis.
In order to avoid the influence of multicollinearity of various influencing factors on the results, the stepwise regression method in SPSS 26.0 software was used to select the independent variables with weak significance. As shown in Table 7, P-values of explanatory variables selected by SPSS calculation are all less than 0.05, and VIF are all less than 7.0.

Space Suitability Test
The traditional panel regression results are tested by LM and Robust LM to determine whether to conduct spatial panel analysis and determine the optimal Spatial econometric model. It can be seen from Table 8 that Moran's I passed the 5% significance test, indicating that it is suitable for spatial panel model analysis; The LM (lag) value is 36.809 and significant at the level of 1%, and the LM(error) value is 66.925 and significant at the level of 1%. It shows that the spatial durbin model (SDM) is superior to the spatial error model (SEM) and the spatial lag model (SAR), and is more suitable for the analysis in this research; The LR test results and Ward test results both rejected the original hypothesis at the significance level of 1%, indicating that SDM model cannot be degenerated into SEM and SAR model. The p-value result of Hausman test rejects the original hypothesis of random effect at the significance level of 1%. Therefore, this study chooses the fixed-effect spatial dobbin model for analysis.

SDM Model Analysis of Fixed Effects
By analyzing the three SDM models with fixed space, fixed time and fixed time and space, the SDM model with the best fixed effect is determined. The results are shown in Table 9.
Compared with the three fixed-effect models, the R2 value of SDM model with time-fixed effect is the largest (0.6187), which is significantly larger than the other two models, indicating that this model has the best fitting degree. In addition, the coefficients of most variables in the time-fixed effect test results have passed the significance test. Based on the results of the model, the spatial Dubin model with fixed time effect is the most suitable model for the study of the spatial effect of the national green economy development level.
By analyzing the regression results in Table 10, it can be seen that scientific and technological progress can significantly improve the development level of green economy in the region. From specific indicators, the level of green economic development will increase by 0.145% for every 1% increase in R&D expenditure. The development level of green economy will increase by 0.096% for every 1% increase in the number of patents granted. The scientific and technological progress can not only provide more advanced technical support for the treatment of pollutants, but also realize energy conservation and emission reduction at the source of pollution and promote the high-quality development of regional green economy. However, for every 1% increase in the contribution rate of high-tech industries, the level of the green economic development will drop by 0.089%. This indicates that the high profit brought by the development of high-tech industry at the present stage has not promote the improvement of the green economy level. The reason may be that environmental governance requires a lot of investment, and the benefits obtained are hard to measure. Although the huge profits of high-tech industry have promoted the progress of the local economy, they have not effectively improved the local environmental quality, indicating that higher development requirements for green sustainability should be put forward for the high-tech industry. The level of opening to the outside world has significantly improved the level of the green economy development. From a nationwide perspective, the improvement in the level of opening to the outside world has promoted regional trade, thus promoting the rapid development of local economy. Social capital can significantly promote the development of green economy. Energy consumption plays an important role in restraining the development of the green economy in this region. Environmental improvement and urbanization level have no significant impact on the development level of green economy. corner marks * * *, * *, and * are significant at 1%, 5%, and 10%, respectively, the same below From the perspective of the spatial lag effect of each variable, the spatial lag effect of R&D funding intensity, the number of patents granted, the degree of opening to the outside world, the marriage and divorce rate, and the energy consumption are negative, while the spatial lag effect of urban sewage daily treatment capacity and urbanization level are positive, but not significant, indicating that changes in the above variables in other regions will not have a substantial impact on the development level of green economy in this region. The spatial lag effect of the contribution rate of high-tech industries is significantly positive. The reason may be that the huge profits brought by the high-tech industries in the surrounding areas have driven the economic development of the surrounding areas. The spatial spillover effect of water consumption is significantly negative, which indicates that water consumption in other regions significantly inhibits the development of green economy in this region. The reason is that the conditions of water resources in adjacent areas often influence each other, and improper utilization of water resources in adjacent areas will affect the utilization of water resources in this area, thereby inhibiting the development of the green economy in this area.

Effect Decomposition of Spatial Dubin Model
As the spatial lag coefficient of some factors affecting the development level of green economy is not significant, the regression coefficient calculated above cannot directly reflect its spatial effect. This study further analyzed its direct, indirect and overall benefits. Among them, the direct effect indicates the influence of this influencing factor on the development level of green economy in the region, the indirect effect indicates the influence of this factor on the surrounding regions, and the total effect indicates the overall influence of this factor on the development level of green economy. From the perspective of economic development, the total effect of contribution rate of high-tech industry, private economic development level and degree of opening to the outside world is significantly positive, indicating that the above three influencing factors promote the development of China's green economy macroscopically. Among them, the direct effect of each influencing factor is significant, the direct effect of contribution rate of high-tech industry is significantly negative, and the other four variables are significantly positive; This shows that technological progress, opening up to the outside world and the development of private economy can promote the development of green economy in the region, while the high profit brought by the development of high-tech industry does not promote the development of green economy in the region. The indirect effects of Contributing Rate of High-Tech Industry and Private Economic Development Level are significantly positive, while the indirect effects of The Number of Regional Patents Per 10,000 People are significantly negative; The reason may be that the industrial development in the region has driven the rapid economic development in the surrounding areas, while the index of patent license number indicates that large cities attract a large number of high-quality talents, resulting in the shortage of talents in the surrounding areas, thus inhibiting the development of green economy in the surrounding areas.
From the perspective of social capital, the direct effect of Marriage and divorce rates is positive and significant at 1%, while the indirect effect and total effect are not significant. From the perspective of governance efficiency, the total effect of water resources consumption and total energy consumption per unit of gross industrial output value is significantly negative, indicating that these three factors generally inhibit the development of green economy in China; The direct effect of resource consumption is significantly negative, while the direct effect of sewage treatment capacity is significantly positive, indicating that the inefficient use of resources inhibits the development of green economy in the region, and environmental management can promote its development; The indirect effect of the indicator of water resources consumption per unit of gross industrial output value is significantly negative. The reason is that the available water resources in the surrounding areas are often located in the same river basin, and the improper use of water resources in this area will seriously affect the surrounding areas.

Conclusions
Based on the panel data of 30 provinces in China from 2005 to 2020, this paper measures the development level of green economy in each province and city, and analyzes its spatial spillover effect by using spatial dobbin model, and draws the following main conclusions: 1). From 2005 to 2020, the development level of green economy in 30 provinces in China has a steady trend of improvement, with Xinjiang, Hainan, Tianjin and other places increasing more, Yunnan, Guangxi, Shaanxi and other places increasing less. In recent years, the top-ranked provinces are Beijing, Guangdong, Hainan, Shanghai, Tianjin, Zhejiang and other places.
2). The green economy development level of 30 provinces in China has a significant global spatial positive correlation. The development level of green economy in each province is clustered, mainly distributed in low-low clustering areas. This shows that the development level of the green economy is not only influenced by local economic development, social capital and governance efficiency, but also by related factors in the surrounding areas.
3). By constructing spatial dobbin model to decompose its spatial spillover effect, we can see that scientific and technological progress, private economy and the degree of opening to the outside world are the key factors to promote the high-quality development of China's green economy, while the extensive utilization of resources hinders the development of regional green economy. The direct effect of openness is the highest, followed by social capital and patent license. The profit of high-tech industry has positive spatial spillover effect with the development level of private economy. Under the close interaction of population, information, capital and materials, it can form the synergistic coupling effect of regional green economy development can be formed. Patent licensing and water resources consumption have negative spatial spillover effect. The spatial spillover effect of other factors is not significant.

Suggestion
At present, there is still much room for improvement in the development level of China's green economy. In terms of time, the overall development level has a tendency to improve steadily. From the perspective of space, the imbalance of green economy development is even more serious. The environmental planning formulated and implemented by the government needs to scientifically deal with the relationship between economic development and environmental protection scientifically. The following aspects should be paid attention to when formulating and implementing regulatory policies.
1). Strengthen the coordinated transformation and development of the regional economy, formulate ecological regulation policies considering financial development, and create a good living environment. The empirical results show that scientific and technological progress, opening to the outside world and the development of private economy all play a positive role in promoting the development of green economy, while the spatial spillover effect is obvious. This shows that a good green ecological environment and a better life for the people complement each other. All regions should promote the coordinated development of regional green economy from the perspective of new development, take the development of green economy as the guide, and promote the cooperation of enterprises and technical exchange between neighboring regions, so as to achieve a "win-win" situation between ecological environment protection and economic growth.
2). Emphasis on "green production", energy conservation and emission reduction, and adjustment of industrial structure. According to the empirical results, energy utilization efficiency and environmental governance capacity significantly affect the development level of regional green economy. Therefore, we should promote the effective utilization of resources, establish our own standards in the industry, build a low-pollution and low-consumption industrial development model, and form a circular economy model of the whole industrial chain.
3). Pay attention to the relationship between scientific and technological progress and environmental protection, so that technological progress can truly serve the development of green economy. According to the empirical results, the three indicators related to scientific and technological progress have significant direct and indirect effects on the development of regional green economy, which indicates that scientific and technological innovation is an important indicator for the development of green economy in this region and its surrounding areas. Among them, the total effect of contribution rate of high-tech industry is positive and significant, but the direct effect is negative and significant, which indicates that the related industries should be strictly supervised and the green sustainable development with high requirements should be put forward. Scientific and technological innovation plays a fundamental and strategic supporting role in the development of the green economy. Therefore, the government should pay attention to the investment of scientific research funds, but at the same time, it should standardize the ecological civilization of scientific research development, encourage the integration of the two, and build an ecological environment scientific and technological innovation system.


Ranking of Districts of Andhra Pradesh According to Sustainable Livelihood Security Indices Values

By computing the Sustainable Livelihood Security Indices, the current research study aimed to estimate and rank the sustainability in development and livelihood status of thirteen districts in Andhra Pradesh (SLSI). SLSI is a compound index made up of three indices: ecological security index (ESI), economic efficiency index (EEI), and social equity index (SEI). It is one of the most comprehensive yet straightforward indexes for assessing long-term security in livelihood in the research domain. For the time periods of 2006, 2016, and 2017 the districts of Andhra Pradesh were ranked in three categories depending on their level of development: high, medium, and low sustainable. According to the findings, the districts viz. West Godavari, East Godavari, Guntur, Krishna, and Prakasham have stayed in the high sustainable category without modification among time periods 2006, 2016, and 2017. Chittoor, S.P.S. Nellore, and Y.S.R. Kadapa were observed to be developing at a medium pace. In all three years, the districts of Srikakulam, Vishakapatanam, and Vizianagaram were observed to have a low degree of sustainability in SLSI category.

INTRODUCTION
In 1972, during the UN Conference on the Human Environment in Stockholm, the conceptsustainable development earned its first substantial international acknowledgment. The UN coined the term "sustainable development" in its document "Our Common Future" [1]. Sustainable development, according to the World Commission on Environment and Development (WECD), is "development that meets current human needs without jeopardising future generations' ability to satisfy their own needs." It encompasses the interconnection and interplay of developments in environmental, economic, and social elements [2]. The concept of sustainability is always articulated in juxtaposition with the concept of livelihood. Livelihood is defined as all actions that are essential to lead a life by an individual or households through acquiring all the basic necessities like food, clothing, shelter, water etc., on a sustainable basis, Shyamalie et al. [3]. Sustainable livelihood explains the connection between people and their livelihood, Barela et al [4].
As the resources are scarce, there exists an imbalance between availability and actual requirement of goods and services created in catering the ever emerging needs of the population.
This causes economic, environmental and social unsteadiness which consecutively affects the sustainability in development in a region or a nation. Sustainable livelihoods concept provides a ray of hope in attaining the development in a sustained manner with greater socio-economic equity. To attain sustainable development goals, a region's sustainability must be improved. Such progress is critical, especially in nations like India where biodiversity is abundant. Several critical elements, including economic, environmental, and social indicators, determine a region's longterm viability.
Swaminathan [5] proposed the SLSI as an operational measure for determining the occurrence of conditions necessary for sustainability in a specific location. The SLSI contains three interacting components that correspond to the three-dimensional idea of sustainability: ecological security, economic efficiency, and social equity.
Analyzing the degree of sustainability with the application of a composite indicator called the sustainable livelihood security index (SLSI) is imperative. This SLSI can be computed using three indicators. The three indicators viz. Ecological Security, Economic efficiency and Social Equity, Amita et al [6] and Amarnath et al [7] were used for calculating SLlSI for Andhra Pradesh state. The fifteen variables chosen to represent the three components or indicators of SLSI are listed below.

Ecological Security Indicators
Ecological security is crucial to control and enhance the resource base of the economy. Six variables included were, 1. Population density 2. Proportion of geographic area under forest 3. Cropping intensity 4. Livestock density 5. Net irrigated area 6. Population growth

Economic Efficiency Indicators
Economic efficiency directs the most efficient use of capital and human resources within the current technical conditions in order to cater the everyday needs of the society. Four variables included under this indicator were, 7. Total food grain yield 8. Total milk production 9. Net sown area 10. Fertilizer consumption

Social Equity Indicators
Social equity ensures a wide sharing of economic benefits to society in form of sustainable and secure livelihoods, particularly for the socio-economically disadvantaged. Five variables included under this indicator were, 11. Literacy rate 12. Female literacy rate 13. Rural road connectivity 14. Number of the commercial bank branches 15. Number of the primary health centres Andhra Pradesh state was selected for the study because it has a high level of inequality, poor administration, over-exploitation of natural resources, and a rapidly growing population. These have posed a threat to the state's natural equilibrium, as well as socio-economic status of households in various districts. The state's effective development of sustainable agriculture has been jeopardised by constantly rising inequality. The goal of this study is to create composite indices of three indicators: ecological security, economic efficiency, and social equity, to measure the SLS in districts of Andhra Pradesh. The sustainable livelihood security index is an effective instrument for assessing sustainability because it is simple, easy to understand and informative. It is useful for developing policies and plans to improve people's livelihood security by introducing new income-generating tactics and increasing their knowledge.

MATERIALS AND METHODS
The SLSI will be calculated using the ratio approach mentioned below, by using three indices: ESI, EEI, and SEI [8].
The SLSI was computed using the following ratio methodology, Singh et al [9]. Where, The equal weights of indices of corresponding representative variables will be used to compute three compound indices of SLSI, namely, ESI, EEI, and SEI, Krishna et al, [12]. The arithmetic mean of its component indices was used to generate a composite index, SLSI. The values range from 0 to 1. A score around 0 indicates a poor level of sustainability, whereas a value near one indicates a high level of sustainability, Sridhara et al. [13]. The research gap identified will be helpful to assess the level of indices to livelihood security for chosen districts. The parameters will be developed for assessing the indices to livelihood security with respect to the various kinds of livelihood security viz., food, education, economic, health and social security.

RESULTS AND DISCUSSION
From the In the year 2017, from Table 6. Districts fall under low level of sustainability were Anantapur, Srikakulam, Vizianagaram and Vishakapatanam. Followed by, districts with medium sustainability viz. Y.S.R. Kadapa, Kurnool, S.P.S. Nellore and chittoor. Further, remaining five districts viz., East Godavari, West Godavari, Guntur, Krishna and Prakasham were classified as highly sustainable. The similar results were also observed by Deshmukh et al. [14] and Mahima et al. [8] from their respective studies. Here, districts were classified for better interpretation under three levels of development as high, medium and low sustainable based on the overall Sustainable Livelihood Security Index score.  However, there identified no significant changes in the performance of districts. In addition, the SLSI category for Anantapur district was identified as medium. However, the district was deemed to be in the low SLSI category in 2017. Kurnool district, which had been ranked under low level of development improved to a medium level in 2017. In all three years, the districts, Srikakulam, Vishakapatanam, and Vizianagaram were observed to have a low degree of development in SLSI category. SLSI highlights the kind and form of policies that should be implemented in each study area for improving livelihood security alongside the overall development priorities.


Underemployment Following the Great Recession and the COVID-19 Recession

The underemployment rate, the percent of employed people who are working part-time but prefer to be working full-time, moves closely with the unemployment rate, rising during recessions and falling during expansions. Following the Great Recession, the underemployment rate had stayed persistently elevated when compared to the unemployment rate, that is, until the COVID-19 recession. Since then, it has been consistent with its pre-2008 levels. We find that changes in relative industry size account for essentially none of the underemployment rate increase after the Great Recession nor the underemployment rate decrease after the COVID-19 recession. Based on this finding, we do not expect the underemployment rate to revert to its pre-COVID-19 levels if industry composition reverts to its pre-COVID-19 structure.

People working part-time who would prefer to be working fulltime comprise a large group in the United States. These people may be referred to as working "part time for economic reasons," "involuntarily part-time," or "underemployed." 1 Since 1994, there has been an average of 5.4 million underemployed workers in the United States, rising to more than 9 million during the [2008][2009] Great Recession and to more than 10 million in the 2020 recession caused by the SARS-CoV-2 (COVID-19) pandemic.
In this Commentary, we study the behavior of US underemployment during recent business cycles. In particular, we focus on the underemployment rate, the percent of employed people who are working part-time but prefer to be working full-time. 2 The underemployment rate moves closely with the unemployment rate, rising in recessions and falling in expansions. Consistent with earlier research, we show that the underemployment rate shifted up when compared to the unemployment rate during and following the Great Recession. 3 However, we also document a more recent phenomenon, that the underemployment rate has shifted back down after the COVID-19 recession. That is, the unemployment and underemployment rates are now at levels consistent with their pre-2008 behavior.
Earlier research suggested that changes in industry composition, or the number of employees in each industry compared to the total economy, could explain the persistently elevated underemployment following the Great Recession. 4 Because changes in industry composition reflect changes to the structure of the economy, an implication of this prior research is that elevated underemployment may be a near-permanent feature of the economy. We build on this earlier research by studying the effects of industry composition following both the Great Recession and the COVID-19 recession. We decompose changes in the total underemployment rate into changes in industry-specific underemployment rates (holding employment shares constant) and changes in industryspecific employment shares (holding underemployment rates constant). In contrast to earlier research, we find that changes in industry composition do not account for the increase in the underemployment rate following the Great Recession. We also find that changes in industry composition do not account for the decrease in the underemployment rate following the COVID-19 recession. Because of this finding, we would not expect the underemployment rate to revert back to its pre-COVID-19 levels if the industry composition reverts back to its pre-COVID-19 structure.
The shifts in underemployment that we document have potentially important monetary policy implications for the Federal Open Market Committee's (FOMC) assessments of labor market conditions. In 2014, then-Chair of the Federal Reserve Janet Yellen noted that elevated underemployment following the Great Recession meant that the unemployment rate alone may understate the amount of labor market slack in the economy. 5 Following the COVID-19 recession, the underemployment rate has dropped across most industries, a situation suggesting that the underemployment rate may not currently be a source of additional labor market slack. These changes in the underemployment rate suggest that it could be useful in assessing the labor market and the FOMC's maximum employment objective.
The underemployment rate and its behavior over recent business cycles Every month, the Bureau of Labor Statistics (BLS) surveys about 60,000 households in the United States, asking many questions about employment and labor force status. 6 Using responses to this survey, the BLS estimates both the number of people who are employed and who are underemployed. In this Commentary, we use BLS data and survey responses to study the underemployment rate: the percent of employed workers who are underemployed. Figure 1 shows the underemployment rate from January 1994 to November 2021. 7 For comparison purposes, Figure 1 also shows the unemployment rate, the percent of people in the labor force who are unemployed, a number which is a popular measure of labor market health. The two rates move closely together, rising in recessions and typically falling in expansions. They both rose to high levels during the Great Recession, and they both spiked in April 2020 because of lockdowns associated with the COVID-19 pandemic. Both the underemployment and unemployment rates began dropping after the Great Recession and the COVID-19 recession, with the drops after the COVID-19 recession being more rapid than after the Great Recession.
We now highlight three features of the underemployment and unemployment rates in Figure 1. First, from 1994 to 2007, the unemployment rate was between 1.4 percentage points and 3.0 percentage points above the underemployment rate, averaging 2.0 percentage points above the underemployment rate during this sample.
Second, the unemployment rate fell more quickly in the expansion after the Great Recession than did the underemployment rate. From January 2014 to February 2020, the month before the World Health Organization declared COVID-19 a global pandemic, sparking the COVID-19 recession, the unemployment rate was between 0.5 percentage points and 1.6 percentage points above the underemployment rate, averaging only 1.0 percentage points above the underemployment rate during this sample. In other words, the underemployment rate was elevated compared to the unemployment rate for more than a decade following the Great Recession. This elevated level of underemployment suggests that the unemployment rate may have been understating labor market slack after the Great Recession (Yellen, 2014).
Third, the underemployment rate has fallen sharply since the COVID-19 recession. As of November 2021, it is at 2.8 percentage points and is similar to levels observed late in economic expansions, such as in 1998, 2006, and 2019. On average from January 2021 to November 2021, the unemployment rate was 2.2 percentage points above the underemployment rate, a difference which is more consistent with the gap observed from 1994-2007 than that after the Great Recession.
To highlight these features, we also show the monthly unemployment and underemployment rates as a scatter plot in Figure 2 using different colors to represent different time periods. Figure 2 shows an upward shift in the underemployment rate from the January 1994-December 2007 period to the January 2008-February 2020 period, with the collection of orange dots mostly being above the collection of blue dots. This upward shift is another way of seeing that the underemployment rate was persistently elevated compared to the unemployment rate following the Great Recession. 8 The March 2020-October 2021 period, shown with green dots, shifts back down and appears more consistent with January 1994-December 2007 than with January 2008-February 2020. 9 This downward shift represents the rapid fall in the underemployment rate compared to the unemployment rate after the COVID-19 recession. November 2021 is highlighted with a red triangle and shows that the current underemployment rate is more consistent with levels from January 1994-December 2007 than from January 2008-February 2020.
The results presented so far indicate that the underemployment rate was unusually high compared to the unemployment rate following the Great Recession; however, they do not address why this is the case. In the next section, we study one hypothesis: the change in industry structure. If following the Great Recession employment shifted into industries with generally higher rates of underemployment, then the underemployment rate could have become persistently higher. Further, if high-underemployment industries such as retail trade and leisure and hospitality were disrupted by COVID-19, then it is possible that the currently low underemployment rate could rise when these industries recover.

Underemployment rate by industry
We begin our study of industry structure by showing the underemployment rates across industries at different points in time in Table 1. 10 Table 1 also shows the total unemployment and underemployment rates. We use averages of Q2 and Q3 data for 2004, 2015, and 2021 in Table 1 for three reasons. First, the total unemployment rate is similar in each of these periods, allowing us to essentially hold it constant when comparing underemployment rates. Second, using the same quarters in each of these years minimizes the effects of seasonality for our underemployment rates, which are not seasonally adjusted for the industries. Third, averaging over multiple quarters can smooth through noise in the monthly survey data.
We highlight three results in Table 1. First, the total underemployment rate is about 1 percentage point higher in 2015 compared to the other periods in Table 1. This is consistent with the upward shift in the underemployment rate compared to the unemployment rate in the January 2008-February 2020 period discussed in the previous section. Second, there can be consistently large differences in underemployment rates across industries. For example, leisure and hospitality, retail trade, and other services have higher underemployment rates than manufacturing and wholesale trade in all periods listed in Table 1. Third, the underemployment rate can vary within each industry over time, and underemployment rates were higher across most industries in 2015 than in other periods shown in Table 1.
Next, we show employment shares, or the fraction of total employees working within a particular industry, in Table 2 for the same time periods as shown in Table 1. We highlight two results from Table 2. First, there is substantial variation in employment share across industry. For example, education and health services is between 6 and 10 times as large as wholesale trade depending on the time period. Second, employment shares of a given industry can change over time. In particular, the employment share of leisure and hospitality (a high-underemployment rate industry) rose from 2004 to 2015, while the employment share of manufacturing (a low-underemployment rate industry) fell from 2004 to 2015. Also, the employment shares of both leisure and hospitality and retail trade (high-underemployment rate industries) are lower in 2021 than in 2015.
The total underemployment rate can be computed as a weighted average of the industry underemployment rates from Table 1, in which employment shares from Table 2 act as the weights. Using this weighted average, we can then decompose the change in the total underemployment rate into two components. 11 The first component is the change in the underemployment rate in each industry, multiplied by an employment share that is held constant. We call this the "underemployment rate contribution." The second component is the change in the employment share of each industry multiplied by an underemployment rate that is held constant. We call this "employment share contribution." Other services 4.6 5.9 5.1   Table 3 has a column for the underemployment rate contribution and for the employment share contribution. The sums in Table 3  Because of this finding, we do not expect the underemployment rate to revert back to its levels following the Great Recession if industry-specific employment shares revert back to the 2015 levels shown in Table 2.
Our findings leave open the question of why underemployment was persistently elevated following the Great Recession. The decomposition in Table 3 indicates that about half of the increase can be accounted for by increases in underemployment rates within the leisure and hospitality and retail trade industries.
Earlier research provides evidence that some underemployment increases in these sectors may be due to the Affordable Care Act. 12 However, this research does not address why underemployment has come down in these sectors since the COVID-19 recession.
Other earlier research has compared how often workers transition from full-time and voluntary part-time employment into unemployment and underemployment. 13 This research finds that worker transitions into unemployment fell to low levels by 2015 but that worker transitions into underemployment remained elevated in 2015. In other words, relatively few workers were becoming unemployed by 2015, but a relatively large number of workers were becoming underemployed in 2015. However, these transition data have not been extended to study the months following the COVID-19 recession.

Conclusions
The underemployment rate, the percent of employed people who are working part-time but prefer to be working fulltime, moves closely with the unemployment rate, rising in recessions and falling in expansions. Following 2008-2009's Great Recession, the underemployment rate stayed persistently high when compared to the unemployment rate. However, following 2020's COVID-19 recession, the underemployment rate fell sharply compared to the unemployment rate, and the underemployment rate is now consistent with its pre-2008 levels.
The persistent increase in underemployment following the Great Recession indicates that the unemployment rate alone may have understated the amount of labor market slack in the economy (Yellen, 2014). However, the large drop in underemployment following the COVID-19 recession suggests that the labor market may be tighter than indicated by the unemployment rate alone.
This Commentary examines whether changes in industry composition account for changes in the underemployment rate, and we find that essentially none of the change in the underemployment rate following either the Great Recession or the COVID-19 recession can be accounted for by changes in industry composition. As such, we do not expect the underemployment rate to revert back to its pre-COVID-19 levels if the industry composition reverts back to its pre-COVID-19 structure.  (2015), and Valletta et al. (2020) use "involuntary part-time." Sum and Khatiwada (2010), Blanchflower and Levin (2015), and Bell and Blanchflower (2018) use "underemployed." 2. Dividing underemployment by total employment controls for trends in the data, such as population or total employment growth, and makes the data more comparable over time. The underemployment rate we use is also used, for example, in Sum and Khatiwada (2010), Blanchflower and Levin (2015), and Valletta et al. (2020). (2013) 5. See Yellen (2014). Related to this idea that underemployment is important for fully understanding the amount of labor market slack, Blanchflower and Levin (2015) and Bell and Blanchflower (2018) provide evidence that elevated underemployment is associated with lower wages. In particular, Bell and Blanchflower (2018) write, "underemployment rather than unemployment lowers pay in the years after the Great Recession."

Valletta and Bengali
6. This survey is known as the Current Population Survey (CPS): https://www.bls.gov/cps/. 7. We begin our sample in 1994 because the BLS changed the definition of "underemployment" in the Current Population Survey in 1994. See Bregger and Dippo (1993) and Polivka and Rothgeb (1993) for further discussion of this and other changes to the Current Population Survey.
8. In a supplemental appendix, we test for a break in the relationship between the unemployment and underemployment rates using Andrews (1993). We find evidence of a break in January 2008, which immediately follows the National Bureau of Economic Analysis's business cycle peak in December 2007.
9. The one green dot at the 4.4 percent unemployment rate in the collection of orange dots is March 2020. The BLS survey for this month was taken right as the COVID-19 pandemic was beginning, so this month looks more similar to January 2008-February 2020 than to April 2020-October 2021.
10. The industry-specific underemployment rate is the fraction of underemployed workers in an industry divided by employed workers in that industry times 100. We measure underemployment with "persons at work," requiring workers to have worked from 1 to 34 hours in the reference week. We measure employment with "employed persons," allowing workers to have 0 hours of work in the reference week if, for example, they are sick or on vacation.
11. This decomposition is shown in the appendix. There is also a third term in the decomposition: the change in the underemployment rate in each industry multiplied by the change in the employment share. This term is generally small, and we do not discuss it further.
12. See Dillender et al. (forthcoming). For some context, the Affordable Care Act requires larger employers to offer health insurance to employees working 30 or more hours per week, but not to employees working fewer than 30 hours per week. Hence, employers may be able to reduce healthcare costs by limiting work weeks to fewer than 30 hours.