Finland buying ganja

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Finland buying ganja

Unless it's prescribed for medicinal purposes, it's illegal to use cannabis in Finland. But an estimated , people still use it in the country every month, and usage has grown over the past few years. This spring a citizens' initiative aiming to legalise cannabis will go to Parliament for consideration after passing the 50, signature mark on World Weed Day, or 20 April, last year. The initiative calls for the legalisation of the use, possession, personal cultivation, manufacture and sale of cannabis in Finland, subject to age restrictions. The initiative also proposes the establishment of a regulatory system for the production and sale of cannabis comparable to those regulating other intoxicants, with the aim of minimising harm to individuals and society. In the EU, Luxembourg and Malta have legalised the possession and cultivation of cannabis for personal use. Kim Kannussaari , from the substance abuse prevention group EHYT, told Yle he wasn't enthusiastic about the prospects of legalisation. Learning ability is impaired, as well as short-term memory. Prolonged use also leads to mental health problems, especially in young people,' he said, adding that lung damage was another aspect to consider in the debate around the issue. But one of the initiators of the project, Coel Thomas Green , said he sees more positives than negatives when it comes to legalisation, including increased tax revenue. Thomas serves as deputy city councillor in Helsinki. In the first three years after the reforms rolled out, Canada's cannabis industry has, for example, funnelled 15 billion Canadian dollars around Thomas challenged the idea that usage increases if a substance is regulated. But usage is already increasing now, even though it is illegal. At the same time, alcohol and tobacco use is decreasing even though they're legal. Prime Minister Petteri Orpo 's NCP governemnt is meanwhile not only against the legalisation of cannabis, but is also against decriminalising it. However, the party's youth wing came out in favour of decriminalising all drugs in , before being shut down by its parent party. That said, Thomas said he thinks it's unlikely the law will be changed to reflect the citizen initiative's proposal. A previous citizens' initiative to decrimialise cannabis reached the required 50, signatures in October Updated on Users with an Yle ID can leave comments on our news stories. You can create your Yle ID via this link. Our guidelines on commenting and moderation are explained here. The company said the job losses are necessary in order to improve the 'profitability and competitiveness of the business operations'. Finland is the second most violent country for women in the European Union. Skip to content. Profitable pot sector? News Foreign policy. News Business. News Domestic news. News Society. News Weather. News Media. Show more.

Is Marijuana Legal in Finland?

Finland buying ganja

Official websites use. Share sensitive information only on official, secure websites. Email: ali. Background: In September , a Finnish political party, the Greens, voted to include cannabis policy reform in their party programme, which would legalise the use, possession, manufacture and sale of cannabis. A rapid public discussion has emerged on different social media platforms, including Twitter. Methods: We downloaded 10 days of Twitter data and prepared it for further text analysis, including sentiment, topic modelling and thematic content analysis. Results: Before the proposal, the average daily number of tweets was approximately However, during the week of the proposal, there was a significant increase in tweet volume, reaching a peak of 6, tweets on a single day, with a daily average of over 2, tweets. Sentiment analysis showed that during the public discussion, the sentiment scores of the tweets were more likely to be positive. Through topic modelling analysis, we obtained the weight of the topic for each tweet, which enabled us to identify the most representative tweets in our corpus. To narrow the sample size for content analysis, we selected tweets that had a topic percentage distribution of over 0. Several positive and negative themes emerged, which were then categorised under broader topics. Similar themes were identified in the most retweeted, liked and commented tweets, which came mainly from known public figures, including politicians, health experts and NGO leaders. Conclusion: Our results show that the discussion was not limited to cannabis legalisation, but instead covered a variety of topics related to drug policy. On Sunday, 12 September , the Greens political party in Finland voted to include as official party policy the legalisation of the use, possession, manufacture and sale of cannabis in Finland Yle News, The policy proposal was passed by a small margin, to This was the first time a political party in the acting coalition government passed such a legislative proposal, and the decision received a great deal of media attention Helsingin Sanomat, ; Yle News, and generated a lively discussion on different social media platforms, including Twitter. Social media platforms provide publicly accessible data that can be used to rapidly capture and describe various contexts and discussions about substances such as cannabis. Previously, Twitter has been used to research health-related discussions and experiences of cannabis use Allem et al. Some research focuses on specific cannabinoid-based products such as edibles Lamy et al. Although cannabis is not the only drug investigated in this way Jain et al. This study is the first to analyse cannabis discourse on Twitter in the Finnish context, focusing on the period when the Finnish Greens voted to officially include cannabis legalisation into their party programme. As mentioned, this was the first time a political party that is part of the acting government of Finland proposed to reform cannabis policy to allow adult use, possession, production and sales, and thus presenting a historical point in time that can be studied with various methodologies. In this study, we use a mixed-methods approach that focuses on the Finnish cannabis discussion on Twitter during a specific period. Compared to other Nordic countries, Finland has the second highest prevalence of lifetime use among people aged 15—64 years Denmark Attitudes toward cannabis have also changed in recent years. Support for legal access other than for medical purposes remains a minority position with great variability among age groups. The adoption of cannabis policy reform by the Greens party in their programme in September can be seen as a response to that pressure and requires research into various social factors involved. This research specifically focuses on what kind of topics and issues were discussed on Twitter when the Greens proposed cannabis reform in Finland, and what arguments for and against it were conceptualised. Which arguments received more reactions retweets, likes, comments, etc. A mixed research methodology was used for this study design. Initially, tweets were downloaded, processed and analysed for descriptive statistics. Subsequently, topic modelling was used to identify the hidden content of the discussions. Finally, thematic content analysis was applied to highly representative texts of topic modelling and the most reactions to tweets the most retweets, liked and commented. A total of 20, posts contained these terms during this period. Timeline of the unique cannabis-related posts on Twitter. A Red dots dashed line represent the time frame of the study includes only original tweets and quotes. B The daily average scores of three Finnish sentiment dictionaries includes only original tweets and quotes. The timeline of cannabis-related posts in Figure 1 shows that before the proposal, the average daily number of tweets was approximately The literature suggests that a higher volume of tweets on cannabis has historically been observed in countries and states in the USA with less restrictive cannabis laws Daniulaityte et al. We then filtered out social bots, which are automated Twitter accounts designed to generate content and engage with legitimate human Twitter accounts. Social bots may skew the data, limiting our ability to accurately describe the public attitude Allem et al. Since the accuracy and results of the bot-detection algorithm change depended on the package and query methods, we investigated the account that had more than 10 tweets to maintain accuracy. This package examines the characteristics of a Twitter account and assigns it a score based on the likelihood that the account is a social bot. This method of detecting social bots is considered cutting-edge and has been used in previous studies on social bots and public health Allem et al. By applying these programmes, we removed posts. The remaining data consisted of 12, tweets. We used a variety of text-processing methods to prepare tweets for data analysis, including the following:. Basic normalisation: This includes changing all tweets to lowercase and removing extra spaces, punctuation and special characters such as brackets. Removal of account mentions: Twitter uses a user hashtag such as account to tag accounts in a post. The name of each tagged account is not important for our research, so they were removed from the tweets. Spacy Montani et al. Removal of non-printable characters emojis : Unicode characters are frequently used in tweets as emoticons or symbols from other languages. Since we were interested in tweet texts in Finnish, we could remove these symbols without significantly altering the meaning of a sentence. Hashtag and URL removal: Hashtags are useful for filtering out tweets related to a group e. Therefore, we avoided using hashtags. URLs embedded in tweets are typically linked to images displayed as embedded images on Twitter and external links to other websites. We discarded it because we could not learn much about a website from its URL. After providing a descriptive analysis, we applied a sentiment analysis. Sentiment analysis also known as opinion mining is the computational study of people's opinions, attitudes and emotions toward entities such as products, services, individuals, issues and events. The goal of sentiment analysis is to determine whether a given text or set of texts is positive, negative or neutral Campesato, In the third stage, we use topic modelling to analyse the content of the tweets. Topic modelling is an unsupervised machine learning technique for finding topics in one or more documents Bengfort et al. There are two underlying assumptions: each document here a tweet consists of a mixture of topics, and each topic consists of a collection of words Campesato, Topic modelling assumes that the semantics of a document is governed by so-called latent variables, which are topics that are more abstract than the actual text. The goal of topic modelling is to discover these latent variables topics that can reveal the primary content of a document or corpus. We used the Latent Dirichlet Analysis LDA topic modelling approach, which is a generative model for assigning topic distributions to documents. Here, each tweet is described by a topic distribution, and each topic is described by a word distribution Campesato, The documents can then be represented by a combination of these topics. A distinguishing feature of LDA models is that topics are not required to be distinct, and words may appear in multiple topics. This allows for a type of topical fuzziness that is useful for dealing with language flexibility Bengfort et al. The LDA algorithm requires manual input of the number of expected topics. We ran the LDA algorithm on the data by varying the topic number from 5 through Perplexity and coherence scores led us to choose 15 topics as an optimum number for our dataset see the Appendix for word distribution scores in Table 1 and Figure 1 for the Intertopic Distance Map. Finally, we assigned the topic with the highest probability to each tweet and categorised the tweets based on the most common topics. Tweets that had a topic percentage distribution over 0. Selected tweets were first categorised qualitatively based on their positive or negative sentiment toward the proposed cannabis policy reform and then analysed for emerging themes. It is important in this process is to make those decisions transparent ibid. The most reacted tweets were translated into English, and the analysis of the content was descriptive with a focus on what kinds of themes emerged and whether the tweets were for or against the suggested cannabis policy reform proposal. Based on the content, we categorised the emerging themes under broader topics around health, policy, environment, and social and criminal justice see Table 2. While Twitter content is arguably in the public domain, users of the platform may not be aware of or always agree that their content might be used for research purposes Jules et al. Although some online discussants might want to be credited openly for their contributions Kozinets, , p. Therefore, in this study, we took steps to protect the anonymity of the discussants when presenting the more qualitative thematic content analysis of the most reacted tweets. For instance, we do not provide the names of the people tweeting, and the tweets have been translated from the original Finnish to English; therefore, they cannot be found by a simple quote search. None of the qualitatively analysed tweets mention personal cannabis use, and thus reporting about them does not pose potential legal harm in the Finnish context where the use of cannabis is criminalised by law. In our view, the more quantitative analysis conducted in this study does not impose harm on any particular individual. However, we did consider it to be important to include the self-reported occupation of the Twitter users of the most reacted tweets to get a better understanding of what types of professions are engaged in the discussion that received the most reactions. While the above-mentioned concealment steps do not fully guarantee that the individual tweets analysed qualitatively and reported here cannot be traced back to the individuals doing the tweeting, we balanced between protecting anonymity and the public nature of the discussion. Before text cleaning, an explanatory descriptive analysis was performed to identify general trends in tweets. When weighted frequencies of hashtags are compared to the number of retweets, likes and quotes, the order of the top 10 lists changes. For example, although poliisi hashtags were used only 32 times in all original tweets, it is in the most top third most retweeted list, the fourth most liked and the third most quoted message with this hashtag. Similarly, the top 10 emojis also show that most of the emotional expressions are related to critical thinking, surprise and enjoyment Table 3. This also coincides with the number of questions in tweets. When the weighted frequencies of emojis are compared with the number of retweets, likes and quotes, the order of the top 10 lists changes. A red fire truck, an essential part of emergency services along with a police car or ambulance red truck , and an ambulance white truck used to transport patients between their home and the hospital are at the top of the retweeted and liked tweet lists. A Nerd Face a smiling yellow face with glasses , often used by people calling themselves nerds in a self-deprecating way, is also a highly visible emoji on these lists Emojipedia, Sentiment Scores are calculated in R programming by categorising and counting the negative and positive words in the text and dividing the difference between the positive and negative word counts by the total word count. As shown in Figure 1B , during the public discussion when the Greens announced their decision to include cannabis policy reform in their party agenda, the sentiment scores of the tweets were more likely to be positive. The overall average sentiment score per day is still less than zero, indicating that the general attitude toward cannabis-related discussions is still negative, but as more people participate in discussions, more positive attitudes emerge. It is expected that those who are in favour of cannabis reform tend to express their ideas more often during this time Mann et al. For instance, public sentiment in tweets is more positive in areas where cannabis is not tightly regulated than in areas where cannabis is illegal van Draanen et al. Based on the keywords and reviewing the sampled tweets, we labelled the 15 topics by approximate themes. Topics 2, 5 and 7 are intertwined, in that they primarily discuss the effects of cannabis legalisation on other substances, the administration of cannabis and policy practices within the criminal justice system. The other topics, on the other hand, are well-separated and represented with clear distances. The topics were described below based on Topic word distribution and Intertopic Distance results see more details in the Supplemental file Figure 1 and Table 1. Simply put, the main discussion topics are the relevance of the initiative, the mismatch between current policy practices and public demand, and other potential policy remedies regarding the prohibition policy. Despite the government's monopoly on sales and the implementation of highly restrictive policies, alcohol consumption is high in Finland. As a result, discussions revolve around alcohol, the effectiveness of restrictions, its impacts on society and its interactions with illegal substances. As a result, the discussions primarily revolve around the legalisation of cannabis, its contribution to the government as a tax and implication-related issues. Thus, while alcohol remains the primary topic, the main emphasis is on the impact of cannabis legalisation on alcohol consumption. The discussion focuses on how legal sales of substances affect society and whether other illegal substances could be legalised in the same way. This includes discussions about how cannabis legalisation will affect the criminal justice system and what kinds of problems could arise. Although the comparison of alcohol remains the primary topic, the discussion is more likely to focus on the substance content. It seems that when opponents created their arguments around the health risks of cannabis use, the proponents highlighted the health risks related to alcohol in society. The topic focuses mainly on exposing the dimensions of substance use and its impact on society. For instance, Alko refers to a company that is Finland's national monopoly in the retailing of alcoholic beverages. It is the only store in the country that sells drinks with an alcohol content greater than 5. The most discussed topic was the comparison of cannabis to alcohol and other substances topic 7; posts , which was followed by discussions about the Greens initiative topic 8; posts , problems with alcohol and cigarette restrictions for youth topic 9; posts , and policy implications of legal sales of alcohol, cigarettes, snus and their effects on other substances and health topic 10; posts topic 5; posts. The least discussed topic, on the other hand, was demanding open discussions for legal cannabis sales such as alcohol or wine topic 12; posts , followed by problems after legalisation topic 6; posts , and the implementation framework and method of legal cannabis sales topic 13; posts. See more details in the Supplemental file, Figure 2. The mixed-methods approach can be especially useful because text data on Twitter can be a rich source of information that can be analysed using both qualitative and quantitative methods to gain a more comprehensive understanding of the phenomenon. For example, the quantitative methods used above allow us to analyse the volume and content of tweets. This type of analysis can provide valuable insights into how Twitter users react to specific events or issues, as well as identify patterns and trends in the data. Through the topic modelling analysis, we obtained the weight of the topic for each tweet, which enabled us to identify the most representative tweets in our corpus. To narrow down the sample size for content analysis, we selected tweets that had a topic percentage distribution over 0. The themes that emerged from analysing the content of the tweets that were categorised as positive were harm reduction, potential tax revenue, increased use despite criminalisation criticism of inefficient policies , support and treatment, criminalisation leads to marginalisation, cannabis is safer than alcohol, cannabis does not cause overdoses, cannabis is already available, prohibition is a failure, regulation has a positive environmental impact, decreased traffic accidents, decreased use by youth, regulation decreases prices, regulation ensures quality control, better access to medical cannabis, separation of drug markets, governmental control and free will. Themes that emerged from analysing the content of the tweets categorised as negative were increased use, harmful effects of drugs, harmful effects of cannabis, lack of support from other political parties, gateway theory, fishing for political points, a majority against decriminalisation and legalisation, investigating drug crimes becoming difficult, increased crime, increased traffic accidents, no need for a new legal drug, harmful effects of smoking and negative environmental impact of cannabis growing. The ability of mediated content to spread widely is related to social media virality. Viral material can spread by interaction with content, including specific user actions such as retweeting, liking and commenting. People are more likely to share information if they see that it has been shared multiple times, contributing to the virality of the content and reinforcing their own beliefs that this is what they should be doing in accordance with social norms Jain et al. The viral posts give a variety of information about the content of the text, the credibility of users and networks, but here we focused on the content of the posts. The top 10 most retweeted posts had a range of 22,—77, retweets, and the top 10 most liked tweets had a range of 1,—3, likes. Similarly, the top 10 most commented posts had a range of 15—72 comments. Our results show that the majority of the top-reacted posts in these categories were against cannabis legalisation. Themes similar to the above were identified in most retweeted tweets. Politicians three Greens, two True Finns, one Christian party, one Left Alliance and one Coalition party member , healthcare professionals and journalists were represented mainly by profession in the most retweeted tweets. While presenting the content of the most retweeted tweets, we also provide details on how many likes and comments they received, which in several tweets also overlapped with the most retweeted ones, although with some variability. For instance, the tweet that received the most comments received 72 comments at the time of data extraction, but less than 50 retweets, which was the cut-off point to include the most retweeted tweets in the thematic content analysis. Thus, the focus in terms of content is on the most retweeted ones as we were more interested in what kind of content gets shared. We included tweets in the top 10 liked and commented tweets even though they had not received more than 50 retweets. The commentary they received was left out of the qualitative analysis because the conversation would need to be reviewed to understand the context Krauss et al. A similar division between positive and negative sentiments toward cannabis policy reform was also found in most retweeted posts, as similar themes emerged here compared to the tweets that had a topic percentage distribution over 0. The prominent theme was health, and especially the harmful effects of cannabis on mental health. School dropout. Moving to harder drugs. Drugs might cause schizophrenia if you have a born susceptibility. It cannot be known beforehand; only by using you will find it out. You will never get better. According to research, cannabis use starting at age 18 has even a 7-times risk for psychiatric disease. Cannabis use starting young has even a 7-fold risk for difficult psychiatric disease. I lived in the UK where cannabis-smoking classmates were in different worlds all the time. For oneself and others. An interesting comparison to the current discussion. While the journalists in the above tweets do not directly comment on the proposed cannabis policy reform initiative made by the Greens, the sentiment in the first is clearly negative towards such reform, as the journalist focuses on the harmful effects of cannabis on mental health as her central theme, while in the latter case, the sentiment is almost neutral but leaning towards positive with the provided links focusing on psychedelic-assisted psychotherapy and comparative harms of other drugs. Juxtaposing cannabis with other drugs, especially alcohol, was a theme in some of the tweets, with a topic percentage distribution over 0. Alcohol causes about a trillion. In every shift multiple times. The cannabis discussion is worried about young people and that legalising the substance makes it attractive. Let me tell you that nowadays, it is already rare for a young person's main drug to be alcohol and not cannabis. The change has already happened. He immediately started smoking cannabis and became psychotic. Perhaps more sarcastic than humorous were tweets from members of other political parties. Much research has been done in the United States. Is there an initiative for the carbon compensation of joint smoking? Wrong signal! Finland and Finns don't need cannabis to mix this soup. Let's continue to keep a sane line in drug policy, that is, a strict line! Attitudes need airing. Most retweeted tweets from currently active party members of the Greens were mainly about announcing the party's decision to adopt cannabis policy reform in their party programme. The Greens thus became the first parliamentary party in Finland to support the legalisation of cannabis. Tragic, but this happened at the time of the current law. Maybe the transition to hard drugs was because they were obtained from the same criminals? The total anonymity of the platform led to more diverse and free discussions, but our study includes not only topic and thematic content analysis, but also the effects of influencers on public discussions. One of the main findings of our study shows that the most reacted tweets came from known public figures, including politicians, health experts and NGO leaders. Nevertheless, they tend to take a position against cannabis reform. Their posts were circulated and discussed during the period when the Greens voted to officially include cannabis policy reform in their party programme. On the other hand, when analysing unique tweet contents, the sentiment scores increased, which shows that aggregated individual tweets have a more positive stance. This is due to social media platforms constituting the main hub for young people aged 18—29 years for expressing their ideas Pew Research Center, Social media platforms enhance unmediated participation and information that challenge credentialed meritocracy power The Palo Alto Group, Although our data do not directly support the argument, some other studies suggest that the disparity between the most and least reacted posts may indicate a generational divide Unlu et al. Younger generations have different perspectives on drugs and long-term drug policy lines than older generations who have been responsible for determining current drug policy Hakkarainen et al. While the public figures representing the older generation tend to be against cannabis legalisation and public opinions endorse them by retweeting, liking and commenting, the younger generation has a more positive view of it Mann et al. Particularly for people who lack either motivation or the capacity to digest the information, perceived credibility may act as a heuristic cue and affect the outcome Jain et al. Our results show that the discussions cover several dimensions of cannabis legalisation, including, but not limited to, relationships between alcohol, legal substances, illegal drugs and cannabis, environmental impacts of cannabis reform, impacts on society and particularly youth, experiences of other countries, impacts on the economy, safety and the criminal justice system, and criticisms of the current prohibition regime Table 1. The polarity of the topics indicates that society here, the online community is aware of the different dimensions of drug policy change. Both pro and con arguments were included in each topic. However, the source and validity of the arguments need to be scrutinised more closely since moral values and political stances tend to influence the discussion. This change reflects changing perceptions towards cannabis more generally as attitudes towards cannabis have become increasingly liberal compared to the pre-social media era Hakkarainen, ; Karjalainen et al. Topic analysis shows that the majority of the topics consists of pro and con arguments for cannabis reform, as well as neutral statements. It seems that people follow discussion threads very closely and post according to their stance. It is a vivid online environment for the public to discuss a social phenomenon reflecting all sorts of ideas. Although it restricts us to draw a concrete conclusion about the general results of the discussion pros vs. Topics include discussions regarding gateway theory, relationships between alcohol, tobacco, snus an oral smokeless tobacco product that is illegal to sell and produce in all EU countries except Sweden; however, since Finland shares a border with Sweden, importing snus for personal use is relatively common and legal and cannabis, problematic drug use and marginalisation of people who use drugs. On the other hand, those in favour of cannabis policy reform present arguments to mitigate the mentioned harms via governmental regulation to, for instance, ensure quality control, separate cannabis from other drug markets and create potential tax revenue. On certain issues, like the environment, the consequences of cannabis policy reform are seen as leading in opposite directions, depending on the individual sentiment, as those holding a positive sentiment argue that reform will have a positive environmental impact and those holding a negative sentiment argue against it. How public sentiments in Finland develop regarding drug policy, in general, and cannabis, in particular, requires further research and monitoring in other social media, and traditional print media as social representations about illicit drug use have several individual and social consequences Savonen et al. Other European countries, such as Germany, Malta and Luxembourg, are moving toward cannabis policy reform Government of Luxembourg, This could also bring about new emerging themes in the Finnish discourse and social representations of cannabis, in particular, and drug policy, in general Savonen, The legal status of cannabis could affect the public discussion, particularly for the supporters of the cannabis legalisation. Previous studies show that countries with less restrictive policies have more cannabis-related tweets and more positive sentiments Daniulaityte et al. In a country where cannabis possession and use are still a classified offense, as in Finland, endorsement in the public sphere with an open identity may have some limitations and reservations. In addition, we only examined messages on Twitter. Thus, the results cannot be generalised to other social media platforms. The short-term analysis of the public reaction to an emerging political event has limitations in determining overall social attitudes. Perhaps a more articulated and less polarised discussion developed in time with a variety of themes representing both points of view, but this would require a longer research period and analysis of the discussion that emerged by individuals engaging in the commentary of the most reacted tweets. Further, those participating in political discussions on Twitter cannot be said to represent the society as a whole e. The number of tweets analysed qualitatively also represents only a small portion of the overall discussion, and it is possible that more themes would emerge including a greater percentage of content in the thematic content analysis. Qualitative data saturation was not, however, our aim. Instead, we wanted to zoom in on the discussion from the wider picture provided by the computational methods. While both approaches have their limitations, when applied together, they give a more nuanced snapshot of the discussion that either one can give on their own. Ten days of Twitter data were downloaded and prepared for further text analysis, including sentiment, topic modelling and thematic content analysis. According to our analysis, the discussion around cannabis policy reform on Twitter during the researched period was rather polarised, and it might be difficult for those with positive or negative sentiments to find common ground on this issue. Analysing the comments of the most reacted tweets and looking at how the sentiments potentially evolved over a longer period of time could show whether participating discussants changed their sentiments when engaging in a public conversation, but this is left for future studies to investigate. We argue that using both quantitative computational methods and qualitative analysis can capture part of that discourse systematically and give insights into the current public sentiment and how it might develop. Supplemental material, sj-docxnad Supplemental material: Supplemental material for this article is available online. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. Nordisk Alkohol Nark. Find articles by Ali Unlu. Find articles by Aleksi Hupli. Open in a new tab. Harm reduction 2. Cannabis is safer than alcohol 4. Cannabis does not cause overdoses 6. Decreased youth use 7. Better access to medical cannabis 1. Potential tax revenue 2. Prohibition is a failure 3. Regulation decreases prices 4. Regulation ensures quality control 5. Separation of drug markets 6. Governmental control 1. Regulation has a positive environmental impact 1. Criminalisation leads to marginalisation Cannabis is already available 3. Free will 4. Increased use despite criminalisation 1. Decreased traffic accidents Negative 1. Increased use 2. Harmful effects of drugs 3. Harmful effects of cannabis 4. Gateway theory 5. Harmful effects of smoking 1. Lack of support from other political parties 2. Fishing for political points 1. Negative environmental impact of cannabis growing 1. No need for a new legal drug 1. Investigating drug crimes becomes difficult 2. Increased crime 3. Increased traffic accidents. Click here for additional data file. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Better access to medical cannabis. Governmental control. Increased use despite criminalisation. Harmful effects of smoking. Fishing for political points. No need for a new legal drug.

Finland buying ganja