Which factors affect the performance of technology business incubators in China? An entrepreneurial ecosystem perspective

To examine which factors affect the performance of technology business incubators in China, the present study proposes an entrepreneurial ecosystem framework with four key areas, i.e., people, technology, capital, and infrastructure. We then assess this framework using a three-year panel data set of 857 national-level technology business incubators in 33 major cities from 28 provinces in China, from 2015 to 2017. We utilize factor analysis to downsize dozens of characteristics of these technology business incubators into seven factors related to the four proposed areas. Panel regression model results show that four of the seven factors related to three areas of the entrepreneurial ecosystem, namely people, technology, and capital areas, have statistically significant associations with an incubator’s performance when applied to the overall national data set. Further, seven factors related to all four areas have various statistically significant associations with an incubator’s performance in five major regional data set. In particular, a technology related factor has a consistently statistically significant association with the performance of the incubator in both national model and the five regional models, as we expected.

Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could facilitate outbreak control efforts and highlight new variants that warrant further study. Here we develop an analytical epidemiological model that infers the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that strongly affect the transmission rate, both within and outside the Spike protein. We also quantify the effects of travel and competition between different lineages on the inferred transmission effects of mutations. Importantly, our model detects lineages with increased transmission as they arise. We infer significant transmission advantages for the Alpha and Delta variants within a week of their appearances in regional data, when their regional frequencies were only around 1%. Our model thus enables the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

Ecological characteristic of dendrosozophytеs of the dendrological park «Oleksandria» of NAS of Ukraine

The ecological characteristics of 159 dendrosozophytes of the dendrological park «Olexandria» are given: 61 of the Pinophyta division, 98 of the Magnoliophyta division; the sozological status is determined according to the latest in- ternational, state, and regional data. In addition, ecomorphs of plants were considered as specific displays of their adaptation to certain discrete abiotic factors of the ecotope. According to the ecological analysis, it was found that the dendrososophytes of the dendrological park are represented by 7 hygromorphs, among which mesophytes predominate (77 species — 49%), which are confined to moderately humid and arid ecotopes of Drymo-, Thamno- and Anthropogenophyton. In general, the rarity component is dominated by mezophilic heliophytic microthermophytic, and mezotrophic phanerophytes.

How to measure the territorial economic impact of the COVID-19 pandemic in contexts with scarce regional data?

In this paper we propose an index to approximate the territorial economic impact of the COVID–19 pandemic in contexts with scarce or outdated regional data, which is often the case in developing countries. This index is based on data that are usually available in most countries: a) the sectoral productive structure of the regions, b) the operational level of each sector, c) the mobility of workers in each region, and d) the possibility of remote work among sectors. The empirical application for Argentina describes the impact of the pandemic on regional production during the second and third quarters of 2020, both for the provinces and labor market areas. Our results show that the regional impact of COVID–19 on private economic activity was highly heterogeneous between and within provinces. The proposed index is also highly correlated with sporadic official data coming from national agencies, while it has a wider geographical and temporal scope, especially in terms of labor market areas.

The Effectiveness of Environmental Spending in China and the Environmental Kuznets Curve

The aim of this study is to determine whether recent environmental spending in China has enabled it to reduce its emissions of greenhouse gases in the context of an Environmental Kuznets Curve-based model and promote sustainable economic development. Following the Paris Agreement in 2015, there has been a coordinated effort to reduce the consumption of fossil fuels and prevent the excessive warming of the climate. The study uses annual regional data across China and a dynamic panel data approach for estimating an EKC model which includes measures of the increased use of fossil fuels and the spending across China to reduce environmental damage The results suggest that the policies have been effective in controlling emissions across a variety of pollutants and that the EKC tends to hold in China but varies according to the pollutant. This suggests that these policies should be continued and where possible, extended.

Aplikasi Dynamic Factor Model untuk Nowcasting Pertumbuhan Ekonomi Regional Menggunakan Data Google Trends di Indonesia

Data mengenai aktvitas ekonomi dibutuhkan secara cepat untuk mengambil berbagai kebijakan, namun data tersebut mengalami keterlambatan publikasi, terutama pada level regional. Data Produk Domestik Regional Bruto (PDRB) dirilis dalam waktu lima minggu sejak triwulan berakhir. Upaya yang dapat dilakukan untuk menyediakan data tersebut adalah melalui nowcasting, yaitu peramalan pada periode berjalan menggunakan variabel berfrekuensi lebih tinggi. Data Google Trends merupakan data berfrekuensi tinggi yang tersedia pada waktu yang sebenarnya. Penelitian ini bertujuan untuk melakukan nowcasting pertumbuhan PDRB menggunakan data Google Trends. Metode nowcasting yang digunakan adalah Dynamic Factor Model (DFM). Hasil nowcasting menunjukkan bahwa model mampu menangkap penurunan aktivitas ekonomi sejak masa pandemi COVID-19. Hasil evaluasi pada dua rentang data menunjukkan bahwa DFM lebih baik pada rentang data yang tidak memasukkan periode adanya guncangan ekonomi.

Information and analytical tools for monitoring the prices of material and technical resources (MTR) of construction

The article deals with features and principles of the price monitoring system for material and technical resources operating now in the road industry. To improve the process of information collection, processing, and analysis concerning the cost of building materials, products, and structures, as well as other types of resources, it is proposed to reinforce a centralized single database of material and technical resources on the basis of regional data. Minimization of costs for data collection, storage, processing and use is possible only with the maximum automation of electronic data collection and exchange, which is realized during the implementation of elements of the centralized monitoring system (CMS) in practice. The architecture, algorithm, and regulations for the formation of a CMS are proposed.

A Review on the Use of Life Cycle Methodologies and Tools in Sustainable Regional Development

This review examines how life cycle methodologies are presently used by regional authorities in their sustainable development programmes. The review incorporates formal methods of life cycle assessment (LCA) as well as non-standardised approaches like life cycle management (LCM). The review describes the sustainability agenda facing regions, and a ‘life cycle toolbox’ that can be used at territorial level. Several parallel literature research methods were used to collect representative examples from around the world of regional life cycle approaches, identifying a variety of common and still-evolving methodologies used to address sustainability issues and applications. Results show that regional use of various life cycle methodologies from the toolbox is growing although scope is often constrained to short life chains, and with limited consideration of secondary (“spillover”) impacts. The conclusions confirm earlier findings that current life cycle tools are not always ideally structured for public sector organisations, with some not yet mature for addressing regional sustainability issues, such as biodiversity, land use and social impacts. Regional data aggregation is currently insufficient for certain methods. Further research is needed to adapt certain life cycle methodologies for regional application, but many available tools could already be further applied than is currently the case.