This study examines the spatial distribution of consumption competitiveness and carrying capacity across regions, exploring their interrelationship and implications for sustainable regional development. An evaluation index system is constructed for both consumption competitiveness and carrying capacity using a range of economic, social, and environmental indicators. We apply this framework to regional data in China and analyze the resultant spatial patterns. The findings reveal significant regional disparities: areas with strong consumption competitiveness are often concentrated in economically developed regions, while high carrying capacity is notable in less populated or resource-rich areas. Notably, a mismatch emerges in some regions—high consumer demand is not always supported by adequate carrying capacity, and vice versa. These disparities highlight potential sustainability challenges and opportunities. In the discussion, we address reasons behind the spatial mismatch and propose policy implications to better align consumer market growth with regional resource and environmental capacity. The paper concludes that integrating consumption-driven growth strategies with carrying capacity considerations is essential for balanced and sustainable regional development.

Purpose: The aim of the study is to apply policy analysis matrix (PAM) to identify international competitiveness of marketing channels and policy impacts of government on each marketing channels. Methodology: Policy analysis matrix is employed to evaluate influences of macroeconomic policy on the Tuong-mango value chain. The study investigated 213 sampling observation of eight main actors in chain. Findings: The findings indicate that although domestic channel 4 exhibits competitiveness (Private cost ratio (PRC) < 1), channels 1, 2, and 3 possess both comparative and competitive advantages (PRC < 1, Domestic Resource Cost (DRC) < 1, and social benefit-cost (SBC) > 1). The government’s strategy on production protection, referred to as Nominal protection coefficient on tradable output (NPCO) 0.16, together with the plan for enhancing added value, denoted as Effective protection coefficient (EPC) 0.14 and Subsidy ratio to producers (SRP) −0.18, place a significant emphasis on the first export channel. The government’s subsidy plan grants preferential treatment to Channel 4 in terms of the pricing of commercially available products, with a Nominal protection coefficient on tradable input (NPCI) value of 0.75. A value-added strategy is implemented for export channels 2 and 3, which have EPCs of 0.76 and 0.85, respectively. Policy implications: If the tradable cost is modified by 20%, there will be a change in the ratio of DRC, SBC, EPC, and SRP. While the EPC does not see a 20% reduction in domestic prices, the DRC and SBC do benefit from this cost reduction. A reduction of 20% in the local cost, coupled with a corresponding rise of 20% in the Free on Board (FOB) price, would result in a significant elevation of the SRP for export channels 1, 2, and 3. Conclusion: This is as evidence for the combination of quantitative is a dynamic tool in the policymaking process to ensure targets, constrictions, and consistent policies for agricultural fields. This permits policies to be changed in steps with an alteration in the economy and priorities set up for the tropical fruits and vegetables field.

In our study, we examined 11 designated tourist destinations in Hungary, which can also be interpreted as tourism products including services, infrastructure and attractions. The National Tourism Development Strategy (NTS) also puts a strong emphasis on digitalisation, as it is an unstoppable process with a significant impact on tourism, thanks to globalisation, increasing competition, accelerating information flows and the dominant paradigm shifts on the demand and supply side. We used both qualitative and quantitative methods in our primary research. First, we conducted in-depth interviews with several important tourism service providers in Hungary on the topic of the digitalisation of tourism. A professional questionnaire, addressed to the offices responsible for destination management was distributed in the designated tourist destinations in Hungary in order to get a more comprehensive picture of the attitudes towards digitalisation in the regions under study. In the course of our work, we managed to classify the destinations into three distinctly different clusters. Our hypothesis—that the higher the digitalisation of a tourist destination is, the higher the average length of stay—was partially confirmed by calculating the regional value of the digitalisation, logistic regression analysis, slope and the individual factor categories.

Improving the competitiveness of tourism destinations is crucial for driving local economies and achieving income growth. In light of this evidence, numerous government departments strive to assess specific factors that impact the competitiveness of tourism destinations, enabling them to issue appropriate new tourism policies that promote more effective forms of tourism business. Therefore, the primary objective of this paper is to investigate how various elements such as tourism resources, tourism support, tourism management, location conditions, and tourism demand influence regional competitiveness in the Northern Bay region of Guangxi Province in China. To accomplish this goal, an online survey was conducted to collect data from 420 visitors who had experienced North Gulf Tourism; yielding an impressive response rate of 95 percent. The findings reveal that all aforementioned factors—namely: Tourism resources, tourism support, tourism management, location conditions and tourist demand—significantly impact destination competitiveness. Notably though, it was found that among these factors influencing destination competitiveness; it is primarily determined by effective local-level management (β = 0.345). Following closely behind are tourist demand (β = 0.133) as the second most influential factor affecting destination competitiveness; followed by location conditions (β = 0.116) ranking third; then comes tourist support (β = 0.03) as fourth in line impacting destination competitiveness; finally with least impact being exerted by available tourist resources (β = 0.016). Consequently, highlighting that regional competitiveness within Guangxi’s Northern Bay area predominantly hinges on efficient local-level management practices thus strongly recommending relevant authorities formulate novel work policies aimed at enhancing levels of local-level competitive advantage within the realm of regional touristic offerings.