Abstract Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.
Muhammad Wasif Zafar, M. Shahbaz, Avik Sinha
et al.
Designing a comprehensive policy framework for ascertaining sustainable development is a problem faced by most of the countries around the globe, and the developed nations are no exception to that. Environmental awareness-oriented policy design for achieving sustainable development goals is a challenge for the developed nations, and there lies the contribution of this study. This study analyzes the impact of renewable energy on carbon emissions, in presence of education, natural resource abundance, foreign direct investment, and economic growth for the Organization for Economic Co-operation and Development countries over the period of 1990-2015. Second generation methodologies are adapted for the empirical estimation. The results show the stimulating role of renewable energy consumption in shaping environmental quality. Education declines carbon emissions. Natural resource abundance and foreign direct investment deteriorate environmental quality. Moreover, the time series individual country analysis also confirms that renewable energy has a positive impact on economic growth. The heterogeneous causality analysis reveals the feedback effect, i.e., bidirectional causal associations among carbon emissions, education, and renewable energy consumption. This empirical evidence suggests that countries should increase investment in education and renewable energy sectors and plan for research and development in renewable energy for ensuring environmental sustainability.
We investigate an intrinsic step-jamming phenomenon at the nanometer scale on Kardar-Parisi-Zhang (KPZ)-like kinetically roughened crystal surfaces that arises during interface-limited steady crystal growth or retreat. Monte Carlo simulations using the Metropolis algorithm on a restricted solid-on-solid (RSOS) lattice model demonstrate that intrinsic step jamming persists on surfaces below 20 nm. In the present model, transport processes such as surface and volume diffusion are excluded, as are elastic interactions, step-step repulsion or attraction, and stoichiometric effects. We show that intrinsic step jamming arises from asymmetric fluctuations in atomic attachment and detachment driven by biased transition probabilities under the SOS restriction, leading to collective step congestion. Asymmetric fluctuations also determine whether adatom or hole clusters grow or recede. This mechanism bears close similarity to jamming phenomena in the asymmetric simple exclusion process (ASEP), including multi-lane variants. In contrast, symmetric thermal fluctuations generate adatom or hole clusters on terraces, thereby suppressing intrinsic step jamming. Possible routes to suppress intrinsic step jamming, including experimentally accessible strategies, are also discussed.
There is a projected increase in offshore wind energy generation in the United States over the next three decades, driven by legislative commitments and government funding. Like other renewable technologies, the construction of offshore wind farms has environmental impacts and spillover effects that must be assessed. Developing offshore wind as a reliable domestic energy source requires a multiregional analysis of economic and environmental effects of constructing projects along lakefronts and coastal regions. Although no commercial offshore wind farms currently operate in the United States, seven states have announced capacity commitments exceeding 28 gigawatts by 2035. This study evaluates the spatial economic and environmental impacts of planned projects by linking the National Renewable Energy Laboratory Offshore Renewables Balance-of-system Installation Tool (ORBIT) with a multiregional input-output model of the U.S. economy developed in the Virtual Industrial Ecology Lab. ORBIT provides capital investment requirements for installation, which are combined with the model to estimate economic spillover effects. Environmental impacts are assessed using a newly developed multiregional greenhouse gas emissions dataset for the U.S. to capture supply chain emissions of offshore wind construction. The five projects analyzed require 16.3 billion dollars in capital investment and generate 27.6 billion dollars in direct and indirect economic impacts across the country. Emissions results show that states active in energy generation are most affected, but impacts can be reduced by decarbonizing the grid. A carbon payback analysis indicates the projects offset construction-phase emissions in less than a year. The framework highlights which states experience the greatest spillover effects in terms of emissions and economic activity required to support offshore wind expansion.
Debates about whether development projects improve living conditions persist, partly because observational estimates can be biased by incomplete adjustment and because reliable outcome data are scarce at the neighborhood level. We address both issues in a continent-scale, sector-specific evaluation of Chinese and World Bank projects across 9,899 neighborhoods in 36 African countries (2002-2013), representative of ~88% of the population. First, we use a recent dataset that measures living conditions with a machine-learned wealth index derived from contemporaneous satellite imagery, yielding a consistent panel of 6.7 km square mosaics. Second, to strengthen identification, we proxy officials' map-based placement criteria using pre-treatment daytime satellite images and fuse these with tabular covariates to estimate funder- and sector-specific ATEs via inverse-probability weighting. Incorporating imagery often shrinks effects relative to tabular-only models. On average, both donors raise wealth, with larger and more consistent gains for China; sector extremes in our sample include Trade and Tourism (330) for the World Bank (+12.29 IWI points), and Emergency Response (700) for China (+15.15). Assignment-mechanism analyses also show World Bank placement is often more predictable from imagery alone (as well as from tabular covariates). This suggests that Chinese project placements are more driven by non-visible, political, or event-driven factors than World Bank placements. To probe residual concerns about selection on observables, we also estimate within-neighborhood (unit) fixed-effects models at a spatial resolution about 67 times finer than prior fixed-effects analyses, leveraging the computer-vision-imputed IWI panels; these deliver smaller but, for Chinese projects, directionally consistent effects.
Pierluigi Colli, Elisabetta Rocca, Jürgen Sprekels
In this paper, we study a phase field model for a tumor growth model of Cahn--Hilliard type in which the often assumed parabolic relaxation of the chemical potential is replaced by a hyperbolic one. We show that the resulting initial-boundary value problem is well posed and that its solutions depend continuously on two given functions: one appearing in the mass balance equation and one in the nutrient equation, representing, respectively, sources of drugs (e.g. chemotherapy) and antiangiogenic therapy. We also discuss regularity properties of the solutions. Moreover, in the case of a constant proliferation function, we rigorously analyze the asymptotic behavior as the coefficient of the inertial term tends to zero, establishing convergence to the corresponding viscous Cahn--Hilliard tumor growth model. Our results apply to a broad class of double-well potentials, including nonsmooth ones.
In this work, we have developed a model for irradiation-assisted grain growth in nanocrystalline UO$_2$ using the MARMOT code. We include the impact of irradiation on UO$_2$ grain growth by coupling a phase field grain growth model with a heat conduction simulation that features a random heat source representing thermal spikes. Our model parameters have been calibrated against experimental measurements at 300 K. The calibrated model predicts grain growth in an irradiated UO$_2$ thin film that compares well with experimental data at 50 K. These results suggest that thermal spikes are the major cause of the irradiation-assisted grain growth observed in the UO$_2$ experiments. They also indicate that irradiation-assisted grain growth is only significant with average grain sizes less than 35 nm, and thus can be neglected when considering fuel performance of typical UO$_2$ fuel pellets.
Abayomi Agbeyangi, Ayodeji Makinde, Isaac Odun-Ayo
Nigeria's remarkable information and communication technology (ICT) journey spans decades, playing a pivotal role in economic sustainability, especially as the nation celebrates its Republic at Sixty. This paper provides an overview of Nigeria's ICT journey, underscoring its central role in sustainable economic prosperity. We explore the potential of artificial intelligence, blockchain, and the Internet of Things (IoT), revealing the remarkable opportunities on the horizon. We stress the urgency of achieving digital inclusivity, bridging the urban-rural gap, and reducing the technological divide, all of which are critical as Nigeria marks its sixtieth year. We intend to prove the invaluable opportunities of ICT for policymakers, business leaders, and educational institutes as Nigeria looks towards enduring economic development in this digital age. Specifically, we envision a dynamic landscape where emerging technologies are set to redefine industries, supercharge economic growth, and enhance the quality of life for every Nigerian.
Carlos Núñez-Molina, Juan Fernández-Olivares, Raúl Pérez
In this work we propose a planning and acting architecture endowed with a module which learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a planner when faced with scenarios with real-time restrictions. We have trained this architecture on a video game environment used as a standard test-bed for intelligent systems applications, testing it on different levels of the same game to evaluate its generalization abilities. We have measured the performance of our approach as more training data is made available, as well as compared it with both a state-of-the-art, classical planner and the standard Deep Q-Learning algorithm. The results obtained show our model performs better than the alternative methods considered, when both plan quality (plan length) and time requirements are taken into account. On the one hand, it is more sample-efficient than standard Deep Q-Learning, and it is able to generalize better across levels. On the other hand, it reduces problem-solving time when compared with a state-of-the-art automated planner, at the expense of obtaining plans with only 9% more actions.
This paper is the result of the author’s study of his involvement in the preparation of the Eastern Sendawar Spatial Detail Plan (RDTR) in West Kutai Regency, East Kalimantan Province. Planning innovation in the preparation of the RDTR at the study location, much can still be done in innovation related to the planning product. This can also be done in the preparation of other spatial plans. If planners understand the potential, problems, and opportunities that exist in the planned area or area, there will always be many opportunities to innovate and build attractiveness in the planning results. The results of the study indicate that some weaknesses need to be addressed, including the preparation of base and thematic maps, classification of guidelines and standards, reasonable preparation time, and synchronization with the system in OSS. It is recommended to do a crash program in preparing base maps and thematic maps, adjusting the guidelines, separating processes with KLHS, and preparing human resources in managing flexible systems on OSS.
The aim of this article is s to show that contrary to the common parlance and to the widespread belief that treats small business as “the backbone of the economy”, in the sense of being the prime motor of wealth and prosperity, therefore the underlying logic is what is good for small business will also help government achieves overall economic policy goals, the prevailing dominant idea that formulates and drives the Greek economic policy is quite the opposite. Based on textual analysis, from Greece’s Structural Adjustment Programs, to the various assessment reports, till the latest “Development Plan for the Greek Economy”, we attempt to reveal that the prevailing idea that penetrates the abovementioned texts is that “small is not beautiful”. Specifically, after indicating a policy paradox regarding the limited financial support that Greek small businesses received or expected to receive despite their vital importance to the Greek economy, we expose the “structural impediment” idea. According to the latter the existence of a large share of small business in the Greek economy is being considered as a structural impediment for economic growth and prosperity. The implication is a policy dictum that favours a form of an evolutionary natural selection process, whereby only those establishments successful enough to grow will be able to survive, thus the vast bulk of the remaining small firms will exit the market.
The development of plans of action in disaster response scenarios is a time-consuming process. Large Language Models (LLMs) offer a powerful solution to expedite this process through in-context learning. This study presents DisasterResponseGPT, an algorithm that leverages LLMs to generate valid plans of action quickly by incorporating disaster response and planning guidelines in the initial prompt. In DisasterResponseGPT, users input the scenario description and receive a plan of action as output. The proposed method generates multiple plans within seconds, which can be further refined following the user's feedback. Preliminary results indicate that the plans of action developed by DisasterResponseGPT are comparable to human-generated ones while offering greater ease of modification in real-time. This approach has the potential to revolutionize disaster response operations by enabling rapid updates and adjustments during the plan's execution.
Nurma Gupita Dewi, Rizki Ridhasyah, Tio Anta Wibawa
The Micro Small and Medium Enterprises (MSMEs) sector is critical pillars of the Indonesian economy. The COVID-19 pandemic has had a negative impact on MSMEs. The existence of activity violation policies, namely lockdown and social distancing, needs to be anticipated by MSME actors because it has changed consumer behavior and business competition. Digital transformation is a solution for MSMEs to survive the COVID-19 pandemic. The objective of this study is to investigate the impact of access to finance on MSME performance moderated by digitalization. This study utilized 83 MSMEs as a sample. Data was collected by distributing questionnaires to MSME owners who were selected as research samples. Data analysis was performed using the Moderated Regression Analysis (MRA) method. The results of the study indicated that digitization significantly strengthens the relationship between financial access and MSME performance. For MSME actors, digitization will also make it simpler to introduce products, boost turnover, and assist with recording and producing financial reports.
Economics as a science, Economic growth, development, planning
Gustavo Ortiz, Eladio Quintana, Eduardo Ortigoza
et al.
This research gathers historical information about the supply and demand of wheelchairs in Paraguay and its projection. Indeed, the main objective of the work, is to determine the market trends of wheelchairs for people with disabilities in Paraguay, with data collected from 2018 to 2021. To reach the proposed objective, a literature review was conducted, consulting primary and secondary sources of information, then an in-depth quantitative and qualitative documentary research was conducted, based on information from public and private organizations related
to the purchase, sale and use of wheelchairs in Paraguay. This research allows to have in a single document, the current status and future trends of the industry. The particular contribution of this study is to provide information from various sources on the commercialization and use of wheelchairs in Paraguay, in such a way that it serves as a source of consultation for different interest groups, such as companies, public institutions, non-governmental organizations that provide services and end users, who in many cases cannot access useful and detailed information.
Economic growth, development, planning, Human settlements. Communities
Ukraine’s trade in agricultural products plays the key role in determining the well-being of its citizens. The objective of the paper is to reveal the structural transformations in Ukraine’s agricultural production, to analyze the geographical and commodity structure of exports with the focus on agro-food products, to identify the top world importers/exporters of the selected agro-food products, as well as to examine Ukraine’s merchandise trade in 2022 and compare it with that of 2021. The data, taken from the State Statistics Service of Ukraine; the Trade Map, developed by the UNCTAD/WTO International Trade Center; the State Customs Service of Ukraine, as well as agricultural policies of the developed countries served as the information source for research, in which various methods have been used, e.g.: economic-mathematical, statistical, comparison, graphical, tabular, method of expert assessments, etc. The results demonstrate the empirical experience of the importance of agricultural exports for the country’s ability to remain in international trade flows in the conditions of military operations in this country. An empirical example of the impact of the withdrawal of one of the leading suppliers of certain types of agricultural products from world trade is also considered, and the impact of some steps on the return of this country to world trade in the context of ongoing geopolitical shifts is determined.
The report considers the dynamics of the global population as the unique case of the Socio-Economic Soft Matter system. This category was introduced for complex systems dominated by mesoscale assemblies, emerging due to the inherent tendency for local self-organization. The hypothesis is validated by studying population growth evolution using universalistic scaling patterns developed in Soft Matter science. It is supported by the innovative derivative-based and distortions-sensitive analysis, showing the extended Malthus-type trend from 10 000 B till ca. the year 1200. Subsequently, the explicit evidence of the powered exponential population rise pattern is shown, with the unique crossover near 1970. Following this year, the population growth systematically slows down compared to earlier trends. Population growth is confronted with global food demand evolution, which changes and also follows an exponential pattern. The rise of networking and innovations are indicated as the driving force leading to the crossover from the Malthus-type exponential behavior to the powered exponential one. It is supported by the analysis of the number of patents for innovations. The authors introduced the derivative-based and distortions-sensitive analysis for the optimal implementation of the powered exponential function for describing dynamic data.
Matteo Straccamore, Matteo Bruno, Bernardo Monechi
et al.
Over the years, the growing availability of extensive datasets about registered patents allowed researchers to better understand technological innovation drivers. In this work, we investigate how the technological contents of patents characterise the development of metropolitan areas and how innovation is related to GDP per capita. Exploiting worldwide data from 1980 to 2014, and through network-based techniques that only use information about patents, we identify coherent distinguished groups of metropolitan areas, either clustered in the same geographical area or similar from an economic point of view. We also extend the concept of coherent diversification to patent production by showing how it represents a decisive factor in the economic growth of metropolitan areas. These results confirm a picture in which technological innovation can lead and steer the economic development of cities, opening, in this way, the possibility of adopting the tools introduced here to investigate the interplay between urban development and technological innovation.
The research is focused on investigating the influence of oil price fluctuation and different selected macroeconomic indicators on the economic growth of selected G20 countries. The research simply applied descriptive analysis technique as well as regression analysis technique by using a random effect model and Pearson correlation analysis technique to investigate formulated objectives. Results of the research suggested that oil prices and GDP in selected G20 countries have a negative relationship with each other. Results of the research further suggested that the broad money, balance of payment and inflation have a negative impact on the exchange rate of selected G20 countries. These results suggest that it is important for selected G20 countries to make sure to decrease the oil rents and maintain oil prices to have overall positive impact on the economy. Overall, results of the research suggest that a decrease in oil prices can be a significant factor for having a positive influence on the economic growth and progress of selected G20 countries.
Benjamin Weder, Johanna Barzen, Frank Leymann
et al.
With recent advances in the development of more powerful quantum computers, the research area of quantum software engineering is emerging, having the goal to provide concepts, principles, and guidelines to develop high-quality quantum applications. In classical software engineering, lifecycles are used to document the process of designing, implementing, maintaining, analyzing, and adapting software. Such lifecycles provide a common understanding of how to develop and operate an application, which is especially important due to the interdisciplinary nature of quantum computing. Since today`s quantum applications are, in most cases, hybrid, consisting of quantum and classical programs, the lifecycle for quantum applications must involve the development of both kinds of programs. However, the existing lifecycles only target the development of quantum or classical programs in isolation. Additionally, the various programs must be orchestrated, e.g., using workflows. Thus, the development of quantum applications also incorporates the workflow lifecycle. In this chapter, we analyze the software artifacts usually comprising a quantum application and present their corresponding lifecycles. Furthermore, we identify the points of connection between the various lifecycles and integrate them into the overall quantum software development lifecycle. Therefore, the integrated lifecycle serves as a basis for the development and execution of hybrid quantum applications.