Unified Analytical Framework Research Articles

The role of specialization in increasing farmers' income: Horizontal or vertical?

AbstractBased on the theory of division of labor and specialization, this article attempts to address the problem of “splitting horizontal and vertical division of labor” by innovatively integrating horizontal specialization, vertical specialization, and farmers' income into a unified analytical framework. Using the data for 416 chicken farmers in four districts and counties of Guangdong Province as the sample, the theoretical framework proposed in this article is tested by constructing multiple linear regression models and structural equation models. The results are as follows: Both vertical and horizontal specialization can bolster the income of farmers, but they have different specific mechanisms of action. Vertical specialization promotes agricultural income through the mediating effect of horizontal specialization. It is evident that the discussion of horizontal specialization of farmers should not be separated from the conditions of vertical specialization. The policy effect of emphasizing horizontal specialization alone may be limited. Especially for those farmers who are limited by their own endowments and objective conditions to increase their horizontal specialization level and production scale in the short term, it is more important to promote their vertical specialization level.

The Impact of Input Trade Liberalization on the Entry of Foreign Firms: Evidence from a Quasi-Natural Experiment in China

This paper integrates trade policy and foreign direct investment into a unified analytical framework, and investigates the effects of input trade liberalization on the entry of foreign firms. To identify the causal effects, we utilize China’s accession to the WTO in 2001 as a quasi-natural experiment, and perform difference-in-difference estimation. The results show that input trade liberalization significantly increases foreign entry. We also find that input trade liberalization not only promotes the entry of new foreign firms, but also restrains the exit of existing foreign firms, thereby contributing to the net growth of the number of foreign firms. The mechanism tests show that increasing variety as well as quality of intermediate input and reduction in marginal cost are the potential channels through which input trade liberalization promotes foreign entry. This paper further demonstrates that institutional environment strengths the positive effect of input trade liberalization on foreign entry, and the promotive effect of input trade liberalization on foreign entry increases with industry import intensity, additionally, input trade liberalization is also conducive to improving the quality of foreign investment.

Incentive or constraint? Comprehensive impacts of green credit policy on industrial energy intensity.

Green credit policy (GCP) has dual attributes of being both an "environmental regulation" and a "financial instrument". Understanding its role in facilitating industrial green transformation is crucial. However, there is limited theoretical and empirical evidence on the impact of GCP on industrial green transformation. This research fills this gap by comprehensively investigating the impacts and mechanisms of GCP on industrial energy intensity (EI) in China, considering both incentive and constraint effects. Theoretically, the environmental and financial impacts of GCP are merged into a unified analytical framework based on a heterogeneous enterprise model. Empirically, diverse empirical methods, including difference-in-differences (DID), difference-in-differences-in-differences (DDD), and mediating effects models, are adopted to examine whether GCP can promote green innovation or accelerate financial constraints. Results show that (1) GCP significantly decreases EI, especially among high-polluting enterprises (HPEs). The impact of incentives is far greater than that of constraints. (2) Regarding the incentive effect, energy substitution and innovation offsets exert a primary influence on reducing EI. (3) The constraint effect is caused primarily by rising financing and pollution abatement costs. (4) Heterogeneity analysis shows that the inhibiting effect of GCP is more significant in non-state-owned enterprises, underdeveloped financial markets, and abundant energy endowments. This paper provides a theoretical framework and new empirical evidence for policymakers to design effective policies for promoting industrial green transformation.

The effect of heterogeneous environmental regulations on the employment skill structure: The system-GMM approach and mediation model.

Environmental regulation has played an essential function in reducing pollution and it also influences the flow of labor. Although studies on employment and environmental regulation have gained prominence, most researches ignore the heterogeneity of regulatory tools and its discrepant impacts on different skilled labor; moreover, few literatures have explored how environmental regulations affect employment. Therefore, this study creatively incorporates environmental regulation, industrial green transformation and employment skill structure into a unified analytical framework, categorizing environmental regulations into command-and-control type, market-incentive type and voluntary type and analyzing the heterogeneous influences of environmental regulations on employment skill structure. Meanwhile, we explore the indirect impact of environmental regulations on the employment skill structure from the mediating role of industrial green transformation. The following are the research findings: (1) From a national perspective, both command-and-control and market-incentive types present a U-shaped association with employment skill structure, and their intensity has not surpassed the turning point yet; while the voluntary type is positively connected with the employment skill structure. (2) From the regional analysis, the findings in central and western areas are consistent with the national results; while the market-incentive and voluntary types show a reciprocal U-shaped connection with employment skill structure in eastern, and their regulatory intensity is in the rising stage of the curve. (3) Industrial green transformation acts as a partly mediator between market-incentive type and employment skill structure, but presents a suppression effect between command-and-control type, voluntary type, and employment skill structure. This paper takes industrial green transformation as the mediating variable, which emphasizes the importance of industrial green transformation and enhances the understanding on the mechanism of environmental regulation influencing employment skill structure. The research results provide theoretical support and significant reference for China in formulating policies to facilitate industrial green transformation, mitigate pollution, and optimize employment skill structure.

Intersectoral transfers and drivers of net CO2 emissions in China incorporating sources and sinks

For China to achieve carbon neutrality, the intersectoral transfers and drivers of net CO2 emissions (NCE) must be thoroughly and accurately investigated. However, it appears that no research has been conducted on NCE from a sectoral perspective. Therefore, this study accounts for the NCE in 24 sectors, including the household sector. Then, an improved environmental semi-closed input–output analysis (ESIOA) method is proposed to integrate CO2 sources and CO2 sinks into a unified analytical framework, which provides an effective tool for direct research on NCE. Using the improved ESIOA method, this study investigates the intersectoral transfers and underlying drivers of NCE in China from 2010 to 2018. The results would serve as a targeted reference for the carbon neutrality path. The results reveal that NCE increased by 28.76 % during this period, with the embodied NCE concentrated in the manufacturing and construction sectors. Intermediate uses, rather than final demand, are responsible for most embodied NCE. The two paths associated with the construction sector were important production chains for the reduction in NCE. The expansion of final demand was the key driver of the increase in NCE, while technological progress played an important role in the decrease in NCE.

Adjunctive three-way concepts from positive and negative concepts in lattice-valued formal contexts

Three-way concept analysis is an integration of formal concept analysis and three-way decision, which has been found wide applications in knowledge representation, cognitive computing and decision sciences. There have been already a huge number of literature on theory and applications on such topics. Most researches on formal concept analysis focus, independently, on specific scenarios such as Boolean formal contexts and fuzzy formal contexts. In this paper, a unified framework for three-way concept analysis is built in lattice-valued formal contexts. The notions of positive concept and negative concept are given in a lattice-valued formal context based on an overlapping function, one of significant operators for information fusion. Two pairs of object induced adjunctive three-way concept operators and attribute induced adjunctive three-way concept operators are introduced. Their properties have been fully investigated. With those operators, the algebraic structures of adjunctive three-way concepts, their extensions and intensions have been explored. Specifically, the local structure within pairs of sub-concept and sup-concept has been finely characterized. The derived results provide ones a way to construct the complete lattice of a lattice-valued formal context or its sublattice by determining all pairs of sub-concept and sup-concept provided that an object fuzzy set or an attribute fuzzy set is given. A case analysis on a real world dataset from UCI repository is presented to verify the rationality and significance of obtained results.

Reputation, Network, and Performance: Exploring the Diffusion Mechanism of Local Governments’ Behavior during Inter-Governmental Environmental Cooperation

The selective behavior of local governments during regional environmental cooperation could generate a diffusion effect through the black box of reputation mechanism. This study incorporates the reputation mechanism, social capital, and environmental governance performance into a unified analysis framework, empirically testing the moderating effect of the implementation rate of environmental cooperative projects (indicating reputation) on the relationship between two types of social capital and environmental governance performance among cities in the Yangtze River Delta (YRD) and Beijing–Tianjin–Hebei (BTH) regions. The inter-governmental environmental cooperation news and policies are collected by Data Capture technology as a dataset, and a set of social-economic data is also adopted. The spatial econometric regression results show that an increase in reputation could both strengthen the leadership and coordination ability (bridging social capital) of the central cities in the YRD and BTH regions, thus improving their environmental governance performance. However, the bonding social capital path could only significantly work in the BTH region, which unexpectedly increases pollutant emission through excessive internal cohesion. The results indicate that a “community of entangled interest” should be constructed among cities within urban agglomerations, which requires local governments to weaken the concept of their administrative boundary. At the same time, in order to avoid excessive internal condensation, a clear division of rights and responsibilities is also necessary during continuous inter-governmental environmental cooperation. We believe that these findings could provide empirical evidence for local governments to avoid failing to the traps of “agglomeration shadow”.

Investigation on how carbon markets and digital transformation affect green innovation: evidence from Chinese listed companies

Carbon markets and the digital economy are gaining popularity continuously. Under this context, corporate green innovation should be urgently guided, which is an important subject to be studied and solved with great urgency to achieve harmonious ecological and economic growth. In this study, the data of 836 Chinese A-share listed companies from 2007 to 2020 are selected, carbon market and digital transformation are integrated into a unified analysis framework, and the green innovation effect and mechanism of the two and their interaction terms are investigated, not showing consistency with the previous studies about the effect of carbon market or digital transformation on green innovation. The present study suggests that (i) both carbon market policy and digitalization level serve as vital factors in boosting green innovation among high-carbon companies, whereas the synthetic effect of carbon market policy and digital transformation inhibits corporate green innovation behavior. (ii) The influences exerted by carbon market policy, digital transformation, and the synthetic effect on green innovation are dependent on property rights, size, and industry of high-carbon companies. (iii) As revealed by the analysis of mediating effects, financing constraints are the main mechanism of action that leads to a negative correlation between the synthetic effect of the carbon market and digital transformation and green innovation. Besides, R&D investment and environmental information disclosure only affect the action mechanism about digital transformation and carbon market policy, whereas they do not affect the synthetic effect of carbon market and digital transformation. Therefore, to promote green innovation and green transformation of high carbon companies, the government needs to flexibly use market-based environmental regulation tools (e.g., carbon market), strengthen the influence exerted by digital technology in improving innovation quality, and flexibly formulate relevant policies in accordance with the heterogeneity of different objects.

A bibliometric review on marine economy: Current status, development and future directions

The growth and management of the marine economy have received significant attention since the United Nations launched the "Our Ocean, Our Future: Call for Action" initiative. However, a lack of systematic review in the field of the marine economy has hindered progress. This study aims to address this issue by offering a unified knowledge analysis framework with bibliometric technology. We comprehensively analyze 6002 records from the Web of Science using software such as CiteSpace, VOSviewer, and ArcGIS for visualization analysis. This paper has drawn some interesting findings. Firstly, the development of the marine economy has experienced three stages: enlightenment, foundation and rapid growth, with the development process showing more and more apparent geographical cluster characteristics. Secondly, "Sustainable development", "Marine climate change", "Marine ecosystems government" and "Comprehensive performance evaluation" are identified as the mature clusters; meanwhile, "Sea level change", "Marine pollution treatment" and "Sustainable energy exploitation" are potential forefront clusters in marine economy. Based on this, the evolution of knowledge structure about marine economy is found, which changed from discussing the utilization of elements to studying the relationship between them to perform collaborative governance. Finally, this paper concluded by identifying research frontiers and future directions for further study, which includes the development of emerging industries, integration of marine and regional economy, protection of marine ecological and so on. This study can help researchers, policymakers and other participants better understand the knowledge structure and research frontiers of marine economy, and discover the future directions.

Abstract A066: Molecular phenotyping in single cell RNA sequencing allows for identification of common populations across studies and platforms

Abstract Single cell RNA sequencing (scRNA seq) has become a prominent tool towards understanding models of prostate cancer. However, there are limited approaches geared towards collating these experiments into a unified framework for understanding discoveries across studies. In our lab, we have performed scRNA seq on multiple organoid models, across a spectrum of phenotypes including amphicrine, adenocarcinoma, and neuroendocrine disease, as well as performed lineage barcode tracing in a subset of these experiments as a means of identifying lineage transitions. Additionally, we have scRNA seq on a robust patient-derived xenograft (PDX) model of metastasis, consisting of experiments from primary and metastatic PDX models collected from multiple tissues including both intact and castrated backgrounds. Along with data generated by our lab, we accessed published scRNA seq studies from other labs which broadened the pool of experiments to include data from cell lines and multiple single cell analysis platforms. In order to perform computational molecular phenotyping of the scRNA seq experiments, we curated gene sets from the literature and applied principal components analysis (PCA) to them as our molecular phenotyping scoring approach which is widely used by previous studies. Because of the diverse background of the studies being incorporated into our molecular phenotyping, we used the bioconductor stack of single cell analysis tools to perform quality control and removal of batch effects. We observe that molecular phenotyping of samples across different backgrounds shows common populations typically associated with proliferation and stemness, and that unique populations present in each sample are usually more associated with terminal differentiation and senescence. In addition to identifying common and unique populations across samples and platforms, we also can assess individual genes on their importance in the molecular phenotyping approach. The use of PCA to generate our molecular phenotyping score allows the rotation matrix to be used as an additional metric when ranking genes for biological importance in concert with commonly used metrics such as variability and deviance. When combining the global metrics with the rotation matrix for each gene set, genes can be arranged in a network to determine key genes for molecular phenotyping, allowing for more concise gene sets that decrease the computational overhead for performing the molecular phenotyping. Overall, our work suggests that a unified framework of analysis can yield new insights into scRNA seq studies by identifying common populations present across sample backgrounds and different scRNA seq technologies. Additionally, genes can be arranged into networks based on biological significance derived from global statistical analysis as well as molecular phenotyping to identify more concise gene signatures, decreasing computational overhead and highlighting genes with excessive contribution to biological state. Citation Format: Brian Capaldo, Michael Beshiri, Juan Yin, Kathy Kelly. Molecular phenotyping in single cell RNA sequencing allows for identification of common populations across studies and platforms [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A066.

The Impact of Utility Model Patent Quality on Export Performance in China: A Moderated Mediation Effect Model

Utility model patent quality is vital for emerging countries to cultivate a strong domestic market and increase openness. Given China’s high-quality development, this study incorporates enterprise utility model patent quality and export domestic value-added rate (DVAR) into a unified analysis framework to explore their relationship using authorized patent data and financial and customs data of Chinese industrial enterprises from 2002 to 2014. The utility model patent data used to support this study’s findings were provided by the Incopat Patent Database, while the enterprise financial data and customs data were supplied by the RESSET Database. The empirical results show that: (i) utility model patent quality significantly contributes to enterprises’ export DVAR; (ii) factor-intensive heterogeneous analysis indicates that the utility model patent quality of capital-intensive enterprises has a more significant promotion effect on DVAR; (iii) the price–cost markup and the relative price of intermediate goods are crucial domestic channels for utility model patent quality to promote DVAR; and (iv) market segmentation moderates the mediating role of the domestic intermediate relative price. Therefore, the government should further improve the utility model system, focus on increasing domestic market advantages, and develop industry-specific patent guidance policies. This study contributes to the literature by exploring the influence of utility model patent quality on enterprises’ DVAR using an optimized utility model quality index, providing policy references for optimizing China’s utility model patent system and upgrading the import gains of enterprises. However, this study has some limitations, and future research should strengthen the subdivision of utility model patents in different industries and explore the economic performance of the combination of inventions and utility models.

Mortality Risk of Hot Nights: A Nationwide Population-Based Retrospective Study in Japan.

The health effects of heat are well documented; however, limited information is available regarding the health risks of hot nights. Hot nights have become more common, increasing at a faster rate than hot days, making it urgent to understand the characteristics of the hot night risk. We estimated the effects of hot nights on the cause- and location-specific mortality in a nationwide assessment over 43 y (1973-2015) using a unified analytical framework in the 47 prefectures of Japan. Hot nights were defined as days with a) minimum temperature () and b) minimum temperature percentile () for the prefecture. We conducted a time-series analysis using a two-stage approach during the hot night occurrence season (April-November). For each prefecture, we estimated associations between hot nights and mortality controlling for potential confounders including daily mean temperature. We then used a random-effects meta-analytic model to estimate the pooled cumulative association. Overall, 24,721,226 deaths were included in this study. Nationally, all-cause mortality increased by 9%-10% [ relative risk , 95% confidence interval (CI): 1.08, 1.10; , 95% CI: 1.09, 1.11] during hot nights in comparison with nonhot nights. All 11 cause-specific mortalities were strongly associated with hot nights, and the corresponding associations appeared to be acute and lasted a few weeks, depending on the cause of death. The strength of the association between hot nights and mortality varied among prefectures. We found a higher mortality risk from hot nights in early summer in comparison with the late summer in all regions. Our findings support the evidence of mortality impacts from hot nights in excess of that explicable by daily mean temperature and have implications useful for establishing public health policy and research efforts estimating the health effects of climate change. https://doi.org/10.1289/EHP11444.

Toward improving the performance of a variable pitch vertical axis wind turbine (VP-VAWT), Part 1: Sensitivity analysis using Taguchi-CFD approach

Vertical axis wind turbines (VAWTs) are feasible to be improved due to lower efficiency compared to the horizontal axis type and suffer from fatigue issues as a result of cyclic aerodynamic loading. The performance of VAWTs can definitely be enhanced by utilizing a variable pitch angle mechanism and specific-efficient airfoils, especially at low tip speed ratios (TSRs). With this background, the scope of the present investigation is 2-fold. In the first part, Taguchi and computational fluid dynamics (CFD) methods will be used to perform a thorough sensitivity analysis. Then, in the second part, a multi-objective optimization problem will be defined based on the results of part-1 to address the mentioned drawbacks. This part proposes a novel procedure to accomplish sensitivity analysis. A MATLAB code was used to automatically manage suitable simulations for implementing the proposed innovative statistical-aerodynamic analysis. Five objective functions are offered, including power indices, structural and fatigue demands. Design variables consist of 12 parameters related to the pitch angle profile and the blade section geometry. The sensitivity analysis was implemented in three main stages: executing various simulations under distinct conditions based on orthogonal tables, assessing the hypothesis of using different pitch angle profiles for each blade instead of the same pitch profile for all blades, and describing sensitivities using signal-to-noise ratio (S/N) analysis. The same pitch profile idea was proved appropriate based on the results. The third stage result would reflect the order of importance of design variables regarding each objective in detail. Finally, the best-simulated cases are reported and different aerodynamic aspects will be explored. By using the proposed modifications, both upwind and downwind power performance could be enhanced. However, the fatigue problem could be reduced by lowering the maximum local moment coefficient. The integrated systematic approach used in this paper could be considered a unified framework for sensitivity analysis of variable pitch wind turbines.

Spatiotemporal Evolution Analysis of the Chinese Railway Network Structure Based on Self-Organizing Maps

Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately and compare the network indices of adjacent periods based on the railway data of different periods, thus failing to integrate the railway network in different periods into a unified framework for evolution analysis. Therefore, this paper used the railway data from 2008, 2010, 2015, and 2019, and analyzed the spatiotemporal integration of the railway network evolution based on the complex network theory and the self-organizing maps (SOM) method. Firstly, this study constructed the geographical railway network in the four years and probed into how the network feature indices changed. Then, it used the SOM method to capture the spatiotemporal integration of the railway network evolution in multi-time series. Finally, it clustered the change trajectory of each city node and unveiled the relationship between the evolution of city nodes and the hierarchy of urban systems. The results show that from 2008 to 2019, the railway network feature indices showed an upward trend and that the expansion pattern of the railway network could be divided into the core–peripheral pattern, belt expansion pattern, strings of beads pattern, and multi-center network pattern. The evolution of the change trajectory of the city nodes was highly related to the hierarchical structure of the urban system. This study helps to understand the evolution process of the railway network in China, and provides decision-making reference for improving and optimizing China’s railway network.

Abstract 2914: iPSC-derived CAR-NK cell therapy: nominating clinical candidate clones through integrated multi-functional analysis

Abstract CNTY-101 is an induced Pluripotent Stem Cell (iPSC) derived Chimeric Antigen Receptor (CAR) NK cell clinical candidate for the treatment of B-cell malignancies. It incorporates six gene edits. A CAR targeting CD19 mediates tumor cell engagement and killing, the homeostatic cytokine IL-15 improves functional persistence, and EGFR acts as a safety switch, providing an antibody target for elimination of the engineered cells, if ever necessary. Further edits are designed to reduce graft rejection due to alloreactivity, including disruption of beta-2-microglobulin (B2M) to prevent major histocompatibility complex class I (MHC-I) surface expression, disruption of class II major histocompatibility complex transactivator (CIITA), and expression of HLA-E fused with B2M. Our data indicate that iPSC lines show significant functional variability dependent on the original somatic cell and donor used for reprogramming. Additional functional variability is induced when the iPSC are genetically edited and differentiated to NK cells. This complexity necessitates careful methodology for nominating clinical candidate clones with desirable functional attributes and presents an opportunity for future refinement. Here, we evaluate 28 clinical candidate clones for CNTY-101, integrating in-process data from genetic engineering, differentiation, and a battery of functional assays into a unified analysis framework. We find that the donor and the number of integrated transgene alleles (ex. monoallelic vs biallelic) have a large impact on clone function. In silico correction for donor and allele number reveals that transgene surface expression correlates with functions including short-term killing, serial-killing, and persistence. We show that IL-15 transgene expression, serial-killing, and persistence correlate along one functional axis, while CAR expression, short-term killing, and cytokine secretion correlate on a second functional axis. In addition, we identify biomarkers measured during the differentiation process that are correlated with functional performance, such as the level of CD94 surface expression and tumor killing. Finally, we discuss the application of functional multi-omics to candidate clone nomination for Century Therapeutics’ pipeline of future products. Citation Format: Matthew S. Hall, Ohad Manor, Liam Campion, Chiamin Bullaughey, Barry A. Morse, Buddha Gurung, Luis Borges. iPSC-derived CAR-NK cell therapy: nominating clinical candidate clones through integrated multi-functional analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2914.

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

The brightness temperature (TB) features extracted from Chang’e-2 Lunar Microwave Sounder (CELMS) data represent the passive microwave thermal emission (MTE) from the lunar regolith at different depths. However, there have been few studies assessing the importance and contribution of each TB feature for mapping mare basalt units. In this study, a unified framework of TB features analysis is proposed through a case study of Mare Fecunditatis, which is a large basalt basin on the eastern nearside of the Moon. Firstly, TB maps are generated from original CELMS data. Next, all TB features are evaluated systematically using a range of analytical approaches. The Pearson coefficient is used to compute the correlation of features and basalt classes. Two distance metrics, normalized distance and J-S divergence, are selected to measure the discrimination of basalt units by each TB feature. Their contributions to basalt classification are quantitatively evaluated by the ReliefF method and out-of-bag (OOB) importance index. Then, principal component analysis (PCA) is applied to reduce the dimension of TB features and analyze the feature space. Finally, a new geological map of Mare Fecunditatis is generated using CELMS data based on a random forest (RF) classifier. The results will be of great significance in utilizing CELMS data more widely as an additional tool to study the geological structure of the lunar basalt basin.

Transport effects in non-Hermitian nonreciprocal systems: General approach

In this paper, we present a unifying analytical framework for identifying conditions for transport effects such as reflectionless and transparent transport, lasing, and coherent perfect absorption in non-Hermitian nonreciprocal systems using a generalized transfer matrix method. This provides a universal approach to studying the transport of tight-binding platforms, including higher-dimensional models and those with an internal degree of freedom going beyond the previously studied case of one-dimensional chains with nearest-neighbor couplings. For a specific class of tight-binding models, the relevant transport conditions and their signatures of non-Hermitian, nonreciprocal, and topological behavior are analytically tractable from a general perspective. We investigate this class and illustrate our formalism in a paradigmatic ladder model where the system's parameters can be tuned to adjust the transport effect and topological phases.

Environment statistics and material flow accounts development in Lao PDR: A case study on multiple data sources integration for a new statistical domain in a lower middle-income country national statistical system

The Lao Statistics Bureau has launched the development of Statistical Information System on the Environment. With its integrated systems approach, incorporating multiple data sources within a unified analytical framework enabling an understanding of tradeoffs and synergies to monitor sustainable development and formulate integrated, evidence-based policies, the System of Environmental-Economic Accounting (SEEA) plays a central role in Lao PDR strategy for the development of environment statistics, as well as for the mainstreaming of their use in policy processes. However routinely producing environmental-economic accounts at the national level by the national statistical office requires addressing the technical and institutional barriers to the integration of these different data sources. In the context of the national statistical systems of a lower middle income country, the case of the development of Material Flow Accounts by the Lao Statistics Bureau with support from the Lao Luxembourg Cooperation Project in Statistics is illustrative of the challenges faced to integrate data from multiple sources into one single database (the number and diversity of stakeholders and domains and the consequent dispersion of data, the difficulty of access, the often poor quality of the data (linked notably to the relative novelty of the statistics involved) etc.) and provides feedback on the solutions applied to overcome the challenges.

Matrix-scaled resilient consensus of discrete-time and continuous-time networks

This paper studies the matrix-scaled resilient consensus problems over multi-agent networks as occurring in computer science and distributed control. Unlike existing works on consensus problems, where the states of agents converge to a common value or reach some prescribed proportions, we take a more general matrix-scaled approach to accommodate the interdependence of multi-dimensional states. We develop a unified analytical framework to deal with matrix-scaled resilient consensus of discrete-time and continuous-time dynamical agents, where the underlying communication network is modeled as a generic directed time-dependent random graph. We propose new distributed protocols to guarantee the matrix-scaled consensus of cooperative agents in the network in the presence of Byzantine agents, who have full knowledge of the system and pose a severe security threat to the collective consensus objective. The cooperative agents feature multiple input and multiple output, and the number and identities of Byzantine agents are not available to the cooperative ones. Our mathematical approach capitalizes on matrix analysis, control theory, graph theory, and martingale convergence. Some numerical examples are presented to demonstrate the effectiveness of our theoretical results.

Conventional Natural Gas Project Investment and Decision Making under Multiple Uncertainties

Similar to many energy projects, the evaluation of investments in natural gas projects is influenced by technical and economic uncertainties. These uncertainties include natural resource characteristics, production, decline laws, prices, taxes, benchmark yield, and so on. In China, conventional natural gas is still the dominant energy source. The investors are mainly large state-owned energy companies. Therefore, it is necessary to include the technical and economic uncertainties, as well as the investment decision and optimization problems of the enterprises in a unified analytical framework. To this end, this paper innovatively constructs such a framework. Using numerical simulations of approaches, the process of investment decision optimization by companies based on technology assessment and price forecasting is visualized in detail. The results suggest that the investment decision of the enterprise needs to consider technical and economic uncertainties in an integrated manner. It also needs to combine the business strategy and social responsibility of the enterprise in order to construct the objective function. With the availability of data, the framework and its algorithms can be used for practical evaluation of investment plans and decision supports for conventional natural gas projects. The framework can also integrate the analytical perspective of the macroeconomic and political environment to bring in a more comprehensive range of uncertainties.